Krakow University of Economics

In the face of growing geopolitical and economic challenges, ensuring food self-sufficiency is becoming a key component of national security. This paper assesses the level of food self-sufficiency in the production of basic agricultural commodities in Eastern European countries affected by instability. Four agricultural markets are examined: cereals, potatoes, cow’s milk, and pork. The study covers seven countries located directly or indirectly within the zone of instability: Belarus, Estonia, Lithuania, Latvia, Poland, Russia, and Ukraine. The analysis focuses on the trade balance in volume terms, consumption relative to production, the food self-sufficiency ratio (SSR), and dietary energy availability (DEP) per capita in 2014-2023. The results indicate that most countries in the region import more pork and potatoes than they export, whereas the opposite is observed for cow’s milk and cereals. The consumption-to-production ratios point to surpluses in cow’s milk and cereals, while SSR values vary considerably across countries. Moreover, all analysed countries maintained dietary energy availability (DEP) above 2,500 kcal per capita per day.

An anti-inflammatory dietary pattern represents a key component of non-pharmacological management in obesity and metabolic syndrome (MetS), as it targets chronic low-grade inflammation, adipose tissue dysfunction, insulin resistance, and disturbances of the gut-metabolic axis. In the present work, we outline a framework for an "omics-based" approach that integrates data on gut microbiota composition and function (metagenomics), adipokine profiles, nutrigenomics, epigenetics, and related transcriptomic and metabolomic layers in order to enable more precise characterization of the metabolic phenotype and to support precision nutrition strategies. The proposed dietary model emphasizes the quality rather than merely the quantity of macronutrients, with particular focus on lipid profile optimization. Specifically, total fat intake is recommended to remain below 30% of total energy through the reduction in saturated fatty acids (SFA), trans fats, and excessive omega-6 fatty acids, alongside increased consumption of omega-3 PUFA (EPA/DHA) and plant-based sources of α-linolenic acid (ALA). Concurrently, greater intake of lean protein sources and low-glycemic-index carbohydrates rich in dietary fibre-particularly fermentable fractions-is recommended. The model also highlights the importance of polyphenols with antioxidant and immunomodulatory properties. To enhance feasibility and long-term adherence, recommendations are structured as flexible food substitutions rather than rigid prescriptions. Further well-designed interventional studies are required to confirm the impact of a multi-omics-based anti-inflammatory diet on both molecular and clinical endpoints.

This study aims to understand the role of CEO’ personal values for environmentally sustainable business practices. It addresses a research gap by exploring the influence of CEOs' psychological factors on business sustainability. The method applied in the study is logistic regression analysis on CEOs and firm-level data of Anglo-American companies, with data on the personal values of CEOs collected using text mining tools. Our sample consists of 139 CEOs and matching companies. We find that Openness to change values and Self-enhancement values act as enablers for corporate sustainability, while Self-transcendent values have a more complex and ambiguous impact. Our study is the first empirical examination of the effects of CEOs’ personal values on the environmental sustainability of companies, focusing on a diverse array of environmental practices and policies implemented by corporations. The practical implications of our findings highlight the importance of aligning CEO recruitment with corporate sustainability goals.

The accelerated decarbonization agenda of the European Union, supported by the European Green Deal, the Fit for 55 package, and REPowerEU, increases pressure on member states to reduce dependence on fossil fuels and expand renewable generation. Poland, whose power sector remains strongly coal-dependent, faces one of the most challenging and capital-intensive transition pathways in the EU. This study provides a comprehensive assessment of the costs and economic viability of Poland’s energy transition, focusing on the feasibility of replacing coal-based electricity generation with renewable technologies. The analysis applies three financial evaluation methods: net present value (NPV), internal rate of return (IRR), and levelized cost of electricity (LCOE). These tools are used to estimate investment costs of selected renewable technologies, assess the potential for coal substitution in the energy mix, and determine the profitability of renewable projects under selected scenarios. The results show that onshore wind power demonstrates the most favorable investment parameters, including the lowest LCOE and the shortest payback period, while photovoltaics exhibit lower profitability in the analyzed conditions. Nuclear energy may serve as a complementary stable source to variable renewables. The findings provide evidence-based insights supporting national energy planning and the design of future policy instruments.

Abstract In this study, we explore the use of Big Data to dynamically optimize ambulance availability and response times in emergency medical services (EMS). Integrating Big Data principles with the Open Source Routing Machine (OSRM) routing engine, a novel algorithm was used to calculate travel times across an irregular grid, creating real-time, colour-coded time-accessibility maps. In contrast with traditional static models, this approach updates dynamically, accounting for road conditions, accidents and other disruptions to minimize delays. By excluding unusable segments from calculations, the algorithm ensures rapid recalculations, maintaining EMS coverage in evolving conditions. Testing showed significant improvements in response time estimation and resource allocation, particularly in urban environments with complex road networks. This real-time mapping tool offers EMS dispatchers an enhanced decision-support system, potentially saving lives by reducing ambulance response times and improving service availability across diverse areas.

The December 2023 EU–Kenya Economic Partnership Agreement (EPA) is hailed as the most ambitious trade deal ever signed by the EU with a developing country in terms of sustainability. This paper examines the EPA’s sustainable development commitments, guided by two research questions: why Kenya chose the EU over other global actors, and whether it is equipped to comply with the EPA’s obligations. The analysis focuses on Annex V on Trade and Sustainable Development (TSD) and related provisions. The EPA embeds binding obligations on labour standards, climate and environmental protection, and gender equality, requiring implementation of ILO core conventions, the Paris Agreement, and CEDAW. It prohibits lowering standards to attract trade and includes institutional mechanisms for cooperation, aid, and civil society oversight. Enforcement relies on general dispute settlement, with limitations. The paper situates the EPA within EU trade policy and assesses Kenya’s legal, institutional, and economic capacity to meet its commitments. While the EPA reflects an ambitious trade for sustainable development model, its success will depend on credible implementation and sustained support.

Ozone (O3) is a reactive oxidant increasingly applied in biomedical settings, yet its dose-dependent effects on innate immune cells, particularly those from non-human species, remain insufficiently defined. Within a One Health framework, this study examined how two clinically relevant O3 exposure regimens (30 µg/mL and 90 µg/mL) affect porcine neutrophils and monocytes isolated from peripheral blood. Cell viability, reactive oxygen and nitrogen species (RONS) production, and the activity of key enzymes (myeloperoxidase, elastase, alkaline phosphatase, arginase) were assessed at 1 h and 24 h post-exposure. The lower dose induced mild functional activation without compromising viability, whereas the higher dose triggered pronounced oxidative stress, enhanced degranulation, and reduced neutrophil viability by more than 60%. Neutrophils exhibited a stronger and more dynamic response than monocytes, which retained viability and differentiation capacity at 30 µg/mL but showed impaired function at 90 µg/mL. These findings highlight the dual nature of O3, where controlled exposure may support immunomodulation, while excessive dosing disrupts cell function. Defining safe and effective therapeutic windows remains critical for future applications.

This work deals with detection of material discontinuities and inclusions in aluminum castings via a Self-Excited Acoustic System through application of artificial intelligence methods. Various feature extraction methods were compared, including mel-frequency cepstral coefficients (MFCC), short-time Fourier transform (STFT), power spectral density (PSD), and both the discrete and continuous wavelet transforms. The study evaluates the efficacy of each method in capturing essential information for inclusion detection within acoustic signals. In addition, principal component analysis (PCA) was employed to reduce dimensionality. The findings contribute to the advancement of quality control techniques for the production of such systems through robust and efficient detection mechanisms. Experimental results demonstrated that CNNs combined with STFT as well as MFCCs achieved the highest accuracy, exceeding 94% classification accuracy. Networks trained on one-dimensional data showed slightly lower but still robust performance. The feature importance analysis revealed that frequencies between 2 kHz and 12 kHz carried the most significant information for defect detection. These findings confirm that the integration of self-excited acoustic measurements with both deep learning and classical machine learning approaches enables reliable, non destructive identification of discontinuities in aluminum castings.

The growing intersection of climate, geopolitical, and cyber risks poses challenges for financial markets, particularly the insurance sector, which must balance security imperatives with sustainability goals. While prior research has examined these risks individually, comparative evidence across regions and insurance segments remains limited. This study examines insurers’ stock price sensitivity to extreme macro risks using event study methodology, AR-GARCH models, and impulse response analysis. By analyzing over 2327 daily stock prices from insurers in the European Union, the United Kingdom, and the United States from June 2015 to May 2024, we find that climate risk drives the strongest stock price reactions and is systematically priced into equity risk premia. Geopolitical and cyber risks exhibit weaker direct effects but remain embedded in risk premia. Moreover, insurance stock returns adjust rapidly to extreme events, without lasting market distortions. These results highlight regional and sectoral differences in risk pricing, providing valuable insights for investors and regulators seeking to strike a balance between financial stability, sustainability and security considerations. • This study examines the impact of climate, geopolitical, and cybersecurity risks on the insurance sector. • Climate risk elicits the strongest stock price reactions in the UK and US. • Geopolitical and cybersecurity risks contribute to positive equity risk premia. • Stock returns adjust quickly to extreme macro risks without lasting market distortions. • Results emphasize climate risk’s increasing financial relevance in the insurance sector.