This paper aims to identify the export competitiveness of agri-food products at the level of the European Union Member States, considering indicators/elements belonging to technical innovation (technical complexity) as well as qualitative elements. The main research problem is to examine the differences in the technical complexity of agri-food exports (EXPY) between EU Member States. Through detailed analysis of product categories and Member States’ performance, we have identified significant differences in the technical complexity of exports. Products such as dairy, meat, cereal and vegetable preparations were associated with high technical complexity, while fruit, tobacco and oils were classified in the low technical complexity category. We also identified Member States with exports characterised by high technical complexity, including Cyprus, Ireland and Luxembourg, and those with low technical complexity, such as Slovakia, Latvia and Croatia. These findings suggest the influence of various factors, such as economic specialisation, natural resources and R&D investment on shaping the agri-food export landscape in the European Union. In-depth analysis of these issues provides significant insights for understanding the economic and technical dynamics in the European agri-food sector.

The aim of this study is to empirically assess how labour market dysfunctions, human capital, innovation and economic development jointly influence the employment rate in the European Union within the framework of SDG 8—Decent Work and Economic Growth. While the link between employment and sustainability is well-recognized, this research addresses a significant gap by identifying how structural inefficiencies, specifically youth inactivity (NEET) and low work intensity, act as primary inhibitors that decouple economic growth from sustainable social integration. Using a multivariate panel regression, the study quantifies the impact of human capital and R&D investment as catalysts for decent work. The findings challenge the traditional growth-centric paradigm, revealing that achieving SDG 8 targets in the EU depends more on the quality of labour market integration and human capital resilience than on overall GDP expansion. This paper provides a robust empirical framework for policymakers to transition from quantitative employment targets to qualitative, sustainable labour integration.

The Republic of Croatia has several rural areas where agricultural production is carried out. Unfortunately, these areas have been experiencing negative trends and decline, leading to concerns about their survival. Despite state policies that are supposed to protect small agricultural producers, these conditions have been threatened by other interest groups and factors. The current state policy measures have proven to be ineffective, which is evident through the numerous barriers that small agricultural producers face when attempting to place their products on the market. To address these issues, a research paper was conducted to highlight the social responsibility of the state to protect small agricultural producers and ensure that they have equal market conditions. The research paper also presents the concrete demographic and economic situation in these rural areas, and regression calculations, trends, and hypothesis testing were used to identify possible future perspectives of development in these areas. The data used in this research paper were collected from various sources such as the State Statistical Office of the Republic of Croatia, the Agency for Payments in Agriculture, and the Ministry of Agriculture of the Republic of Croatia. The research analysis was carried out using mathematical and statistical methods to quantify the collected data and present them in the form of research results. Based on the findings of the research paper, concrete activities were proposed to create assumptions that small agricultural producers can carry out their business activities without hindrance. These activities aimed to protect small agricultural producers and ensure that they have equal market conditions, thus creating a healthy and sustainable agricultural sector in Croatia.

The rapid integration of Artificial Intelligence (AI) into digital commerce and social structures has created a complex perceptual landscape for consumers. While AI is often associated with efficiency and progress, it simultaneously triggers concerns regarding algorithmic opacity and loss of human control. This study investigates the dual role of digital literacy as a predictor of both perceived utility and consumer scepticism, as well as their subsequent impact on the overall perception of AI as an opportunity or a risk. Using secondary data from a nationally representative German survey on artificial intelligence (GESIS, Study No. ZA8719) and employing Structural Equation Modelling (SEM), the research tests a conceptual framework based on the “Privacy Paradox” and technology acceptance theories. The results reveal that digital literacy significantly enhances the perceived utility of AI while concurrently amplifying consumer scepticism. Furthermore, the study identifies a dominant effect of perceived utility on the overall evaluation of AI, whereas the impact of scepticism remains statistically non-significant. These findings suggest a compensatory mechanism where pragmatic benefits outweigh perceived risks in the consumer’s decision-making process. The paper concludes with strategic implications for digital marketing and e-commerce platforms, emphasizing that value creation is the primary driver for overcoming consumer resistance in the digital era.

Agricultural production is important for ensuring food security, supporting many activities of society and building strong trade economy of a country, but can be slightly influenced by numerous factors. Even if exists a lot of studies that analyze the influencing factors of agricultural production in terms of value, investments, climate change, credits, costs of energy and more, in the literature can be observed a research gap regarding the relation between emissions or mean weekly hours actually worked per employed person and value of agricultural production. The identified gap in the specialty literature was covered in this paper. The present paper aims to analyze a series of factors that influence the value of agricultural production, like: climate change, employment, government expenditure and imports, after discussing the theoretical framework about the subject. The methodological approach is based on using several linear regressions, tested with the EViews software. One of the main findings indicates the existence of relationships between both the change of imports and the change of emissions with the value of agricultural production in Romania during 1995-2021. Also, another result indicates a negative influence of temperature change on the value of agricultural production, showing once again that climate change does negatively impact agricultural output and, thus, the sustainable development of Romania. This study could be used further by policy makers to understand on which drivers should act on in developing sustainable policies for Romanian agriculture.

This research study applies five ML models (ARIMA, Prophet, Random Forest, XGBoost and an Optimal Hybrid ensemble) for forecasting monthly precipitation in the Južno-Bački district, Autonomous Province of Vojvodina, Republic of Serbia, by implementing the 73 years of meteorological data (1951–2024). The Optimal Hybrid machine learning model achieves the best performance of all models (R ² = 0.4577, MAE = 17.85 mm, 83.33% CI coverage). Extreme variabilities recorded in August– September (with errors larger than 50%) emphasise the need for supplementation to meteorological data for achieving more optimal results with machine learning model processing. The research results are interpreted not only as statistical performance, but as operational, field, practical potential for smart agriculture implementation. The Optimal Hybrid model is suitable for three scenarios of irrigation (drought/normal/surplus) for the period of 12 months. The August and September Machine Learning models’ forecast errors indicate the fact that climate variables in the form of indices and expert seasonal long term projections still need to be implemented and combined for effective and reliable drought risk minimization and allocation of key resources as drinking water. Only that way system forecasts can be used in smart agriculture irrigation planning with acceptable risk. Additionally, forecast errors in August and September should be treated as diagnostic and trend related forecasts and not as exact point forecasts. The reason for this is high volatility and variability of climate values. Nevertheless the big data forecasts of this type should represent a preventive system for alarming farmers. It should also help to mitigate weaknesses of the systems currently in use that treat the cosequencies (curative methods) and not act preventively. In addition to that, sustainable development, climate change mitigation and adaptation, local alarming systems for farmers, as well as the climate change government strategies and policies in the most climate-sensitive domains such as agriculture can be improved with new technologies, innovations and particularly AI-based systems.