The growing complexity of socioeconomic processes and the ongoing transformation of the public sector necessitate improvements in the quality and effectiveness of management decisions. However, the absence of well-established and methodologically consistent analytical tools for assessing such decisions complicates informed decision-making and limits the transparency and effectiveness of public policy implementation. Against this background, the development of a comprehensive framework for evaluating the effectiveness of management decisions is of significant theoretical and practical relevance. The aim of this study is to provide a theoretical foundation and develop analytical tools for assessing the effectiveness of management decisions in the public sector, thereby enhancing the quality and soundness of public administration processes. The research employs methods of analysis and synthesis, systemic and structural-functional approaches, comparative and content analysis, economic and statistical methods, as well as generalization and scientific abstraction. The study reveals the theoretical and methodological foundations for evaluating the effectiveness of public-sector management decisions and systematizes scientific approaches to defining effectiveness while accounting for sector-specific characteristics. International experience in applying analytical assessment tools is generalized. As a result, an integrated analytical model for evaluating the effectiveness of management decisions is developed, key influencing factors are identified, and their impact is assessed. The proposed tools may be applied to monitor and evaluate public programs and policies and to support evidence-based public governance.

Sustainable economic and social development is inextricably linked to a country’s food security. This issue takes on particular significance in the context of global crises, geopolitical instability, and environmental challenges. Disruptions in logistics, volatile prices on global food markets, and the depletion of natural resources all require a comprehensive approach and strategic decisions at both the state and community levels. The aim of the study is to conduct an in-depth analysis of the current state of economic and food security based on analytical data, identifying key issues and developing proven strategic solutions to address them in the long term. The study focuses on the following areas: enhancing management efficiency; accelerating the transfer of innovative technologies for agricultural development; expanding and intensifying international cooperation to increase security; ensuring socio-economic growth. The use of modern strategic tools and benchmarking methods allows us to identify the basic threats and challenges in the global world. The United Nations Convention to Combat Desertification allows for the analysis of the Global Food Security Index in 2022, which is directly related to countries with different socio-economic statuses. Gross domestic product and key macroeconomic indicators, such as international trade, foreign direct investment, and political stability, are interrelated, as are defense expenditures. Regression analysis has proven this with an approximation equation. The paper pays special attention to the role of the state and its policy on the rapid real implementation of innovations in the agricultural sector. The results of the study can be used to develop strategies capable of ensuring the country’s economic development and changing the level of food security of the state. The results of the study provide a basis for the implementation and practical application of a flexible strategy capable of ensuring stable economic development and food security against the backdrop of contemporary challenges and dynamic global changes.

The monograph highlights pre-war and war-affected trends in the development and functioning of the agricultural sector. It focuses on the systematisation, comparative analysis, and conceptual clarification of established and emerging scientific terminology that reflects the evolution of national and global economic thought, financial and economic institutions, and investment development (support) institutions. The study explores the operations of a specific institutional entity — the average-industry typical agricultural enterprise — introduced to track the progress of bioeconomic approximation between agricultural and construction economics within the framework of Ukraine’s European integration. The research aims to ensure national resilience (in all its forms) during Ukraine’s early post-war recovery from the consequences of the war. The monograph is intended for researchers, civil servants, local government officials, lecturers in financial, economic, and administrative-management disciplines, as well as students of higher education and research institutions, and a wide circle of readers.

Financial risk is also a constant menace to the agricultural industry in Ukraine. Still, a key problem with conventional banking methods is that they cannot reflect the risk dynamics specific to the area. This paper questions the prevailing belief that artificial intelligence (AI) is universal, in the sense that it outperforms conventional econometric models in predicting credit interest rate volatility across 25 administrative regions (2015-2020). We find an empirical paradox: under the comparatively constant national level, the simple Linear Regression model performed more effectively than elaborate algorithms, with an accuracy rate of 82.35, which confirms the effectiveness of the principle of parsimony when measured against macroeconomic conditions. Nevertheless, the benefit of AI will be high in economically complex regions. Deep learning (ANN) and gradient boosting models identified non-linear risk patterns that linear models overlooked in agricultural centers such as Kherson and Dnipropetrovsk, further enhancing predictive performance by as much as 10.6 percentage points. These findings are consistent with the Adaptive Markets Hypothesis, which posits that the utility of technology depends on market volatility. Therefore, we suggest a precision banking model: a hybrid model in which stable areas would maintain linear efficiency, whereas shock-affected areas would use AI-powered risk detection to maintain the stability of agricultural credit in the post-war period.

The purpose of the study was to analyse public administration strategies aimed at stimulating investment in a sustainable economy and to assess the possibilities of using artificial intelligence tools to improve the effectiveness of state supervision of financial markets. The paper examined public administration strategies focused on stimulating investment in a sustainable economy, and legal aspects of implementing intelligent systems in decision-making processes that contributed to ensuring financial security and stability of the financial system in the long term. In particular, attention was paid to the issues of transparency of algorithmic decisions, responsibility for the use of automated systems, and protection of financial and personal data in public financial management processes. The results of the study showed that the integration of digital technologies into the public financial management system significantly increased the effectiveness of financial supervision, improved transparency of operations, and developed sustainable financial instruments. However, the successful implementation of these transformations required improving the regulatory framework and creating mechanisms for regulating the use of artificial intelligence in the financial sector. In addition, artificial intelligence, due to its ability to automate big data analysis and quickly identify risks, has significantly improved the effectiveness of financial regulation. This has created legal challenges, in particular, regarding the definition of responsibility for automated decisions. The results of the expert survey confirmed the importance of integrating digital technologies with legal guarantees in order to ensure the ethics and stability of financial markets. The practical significance of the study lies in the development of scientifically based approaches to the integration of digital technologies into the public financial management system to ensure financial stability, increase investment activity, and support the long-term development of a sustainable economy

This article examines the systemic links between fiscal sustainability, debt policy, and the modernization of economic policy under the European Green Deal. It addresses two interrelated processes: ensuring debt security and financial stability amid growing fiscal imbalances caused by COVID-19, military expenditures, and the energy crisis, and advancing structural reforms aligned with the Green Deal agenda. The study assesses whether public debt and fiscal policy contribute to fiscal unsustainability and evaluates the impact of circular economy pressures on strengthening public balance sheets. The research employs a systemic institutional economic analysis based on official statistics from Eurostat, the European Commission. The scientific novelty of the study lies in the development of an integrated empirical model that simultaneously captures the interaction between public debt, fiscal sustainability, and circular economy indicators. Unlike existing approaches, which predominantly examine these factors in isolation, the proposed model provides a quantitative assessment of their combined effect on fiscal stability in the European Union. Scenario forecasting suggests that if circularity increases to 24% and public debt declines to 80% of GDP by 2030, the fiscal sustainability index could reach 0.98-1.00. The results support updating fiscal rules, refining debt policy, and developing a balanced public financial management model within the EU's 2028-2034 financial framework, demonstrating that financial stability and environmental transformation are mutually reinforcing pillars of sustainable development. The findings are particularly valuable for policymakers of EU institutions, national fiscal authorities, and public finance regulators, as well as for international financial organizations involved in designing debt sustainability strategies and green transition policies, since they provide an empirical basis for integrating environmental and fiscal instruments into a coherent macroeconomic governance framework.

The process of population ageing is one of the significant demographic risks that affects the state of the labour market and long-term economic sustainability. Accordingly, it is relevant to study the concept of Health Capital in the context of the impact of population ageing on financial and economic processes in the country. The purpose of the article is to substantiate the essence of the concept of Health Capital in the context of population ageing and assess its economic impact on the development of the labour market. The hypothesis of the study is that the development of the concept of Health Capital at the national level affects the reduction of macroeconomic risks, in particular, population ageing, by improving the quality of human capital, strengthening the labour supply, and enhancing labour market resilience. To test this hypothesis, economic and statistical analyses and multiple linear regression modeling were conducted. Based on the results obtained, a relationship between the concept of Health Capital and labour market indicators was confirmed. The developed econometric model explains 73.9% of changes in the employment rate. In particular, life expectancy and per capita healthcare spending have a statistically significant positive impact on employment. Healthcare spending as a share of GDP has a negative relationship with the employment rate. This relationship is explained by the fact that an increase in the share of healthcare spending does not always have a positive effect on the labour market. According to the current state of development of the healthcare system, such an increase is associated with macroeconomic imbalances and the military-political situation. Among the analyzed indicators, per capita healthcare spending has the most significant impact on employment outcomes. The findings confirm that Health Capital represents a strategic component of human capital and a significant determinant of labour market resilience and sustainable socio-economic development. Under conditions of martial law and post-war recovery, strengthening investment in healthcare, rehabilitation services, and preventive healthcare, alongside the improvement of financial mechanisms for supporting population health, can contribute to preserving labour potential, sustaining economic activity, mitigating the consequences of demographic ageing, and enhancing the long-term economic resilience of Ukraine.

The aim of the article is to develop an integrated approach to shaping the architecture of an energy efficiency management system for agricultural enterprises, in whose operation the energy factor increasingly determines not only the level of production costs, but also operational resilience, technological continuity, and the preservation of competitive advantages in the domestic food market. The methodological basis of the study is formed by the simultaneous application of a systems approach, structural-resource analysis, elements of scenario modelling of managerial decisions, and energy management tools aimed at the consistent improvement of the energy performance of agricultural enterprises during the crisis of energy infrastructure caused by constant Russian military attacks on electricity generation facilities and transmission networks. On the basis of empirical data, the article substantiates the expediency of applying an adaptive energy management strategy, within which the management of scarce resources varies according to the basic, stressed, and crisis modes of operation of an agricultural enterprise. It is proven that the effectiveness of investments in variable-frequency motor control, automation of drying processes, thermal insulation of production infrastructure, digital dispatching, and load control systems is not limited to direct savings in energy resources, since such solutions simultaneously affect the stability of production processes, the reduction of operational risks, the minimization of agricultural product losses, and the reduction of demand for scarce resources, in particular technological energy. The combination of biogas plants, solar generation, hybrid modules, and energy storage systems is positioned in the study not as an instrument of absolute energy independence, but as a means of controlled autonomy of agricultural production and its ability to withstand existing energy risks. The practical significance of the results obtained lies in the possibility of applying the proposed approach for the analytical support of investment decisions at agricultural enterprises of different scales and with different levels of access to scarce resources. The scientific novelty lies in the integration of strategic, organizational, financial, and technological solutions into a holistic construct of energy efficiency management for agricultural enterprises, taking into account the current state, namely the energy crisis, and uncertainty regarding the future, in the short and medium term, condition of Ukraine’s energy infrastructure.

In the Digital Era of Governance, budgeting entails leveraging data-driven methodologies and digital technology to enhance the effectiveness, accountability, and openness of public financial management. In order to optimize resource allocation and prevent corruption, this transformation entails putting in place digital financial management platforms and systems, integrating data for real-time monitoring, improving public participation through digital tools, and utilizing cutting-edge technologies like blockchain and artificial intelligence. Meanwhile, as our study showed, key challenges, inherent not only in low-income, but even the most developed countries, include ensuring data security, addressing integration issues, developing necessary skills and infrastructure, and adapting organizational culture. The study employs methodology of integrative review, covering the sample of 36 publications of theoretical and case-based nature.

The study examines the impact of environmental and social ESG factors on the cost of capital of companies in the context of sustainable development. Its purpose is to assess how the integration of ESG indicators influences financial efficiency, particularly the weighted average cost of capital (WACC), and to identify industry- and region-specific differences in this impact. The methodology is based on a quantitative analysis of panel data from publicly listed companies for the period 2015–2023, using ESG ratings from MSCI and Sustainalytics. The results demonstrate that stronger ESG performance is generally associated with a lower cost of both equity and debt capital, contributing to improved long-term financial sustainability. The most pronounced effects are observed in capital-intensive sectors such as industry, energy, and mining, where environmental standards reduce credit risks. Regionally, the strongest ESG impact is found in the European Union and North America due to advanced regulatory frameworks. The study proposes methodological approaches and practical tools for integrating ESG factors into financial analysis and corporate strategic planning.

This paper substantiates a methodological framework for cognitive-integration modeling of organizational structures in construction under conditions of digital transformation. The research proceeds from the premise that contemporary construction projects operate in an environment of high dynamism, uncertainty, and intensified information flows, which fundamentally alter the nature of managerial interactions and temporal coordination. The organizational structure is conceptualized as a system of interrelated managerial, informational, and temporal elements whose interactions can be formally described using cognitive networks with weighted and timelagged influences. The study proposes a set of quantitative indicators for assessing cognitive-integrative coherence, digital integration effects, cognitive inertia, and time controllability of construction projects. These indicators enable the transition from descriptive analysis to formalized evaluation of organizational effectiveness, allowing the identification of critical zones of stability, boundary states, and loss of controllability under organizational disturbances and cognitive constraints. An integrated analytical model is developed to aggregate partial indices into a corrected integral efficiency indicator, reflecting the systemic capacity of organizational structures to ensure stable project implementation in a digitally transformed environment. The results provide a structured methodological basis for analyzing the impact of digital tools on synchronization of managerial decisions, reduction of temporal lags, and adaptive stability of construction projects. The proposed approach supports scenario analysis and comparative evaluation of organizational configurations, contributing to the advancement of decision-support methodologies in construction management under digital transformation conditions.

The shift of economic systems towards circularity is perceived as a priority in terms of recovery from the crisis and ever more macroeconomic instability. Closed loop models are seen less as a tool of environmental reconstruction, but rather, a structural mechanism to enable economic modernisation and sustainability at large. Conversely, the quantitative estimation of the effects of circularity on broader – macroeconomic – dynamics are still meagre, especially in terms of a more comparative cross-country dimension. Our purpose with this study is to focus on the development and testing an integrated Grid-locked technique to empirically estimate the impact of circularity on the economic sustainability of the European Union, in the crisis haven, “ad virum”. Recovery path. The balanced panel comprises the EU-27 for 2015–2024 (270 country-year observations). The countries in the sample are heterogeneous in significant ways: the gap in real GDP per capita exceeds three times; the share of industry in GDP ranges from about 15–30%; the level of urbanization ranges from about 55% to more than 80%; circularity indicators show more than twofold cross-country differentiation. Together, this warrants the use of a panel model with country and time fixed effects. Our methodology proceeds on the formation of composite indices of circularity and economic resilience based on minmax normalizing indicators across the whole panel of observations and then econometrically estimating them using a fixed effects model (Stata 17.95% confidence interval, clustered robust standard errors, Hausman test for specification selection). The circularity index combines resource productivity, recycled materials use, waste recycling, environmental expenditures and the share of renewable energy; the economic sustainability index combines real GDP per capita, employment stability and investment activity. The results show a statistically significant positive correlation between the integrated circularity index and the economic resilience index for the years 2021–2024 when controlling for structural characteristics of economies (industry structure, investment, urbanization) and for time invariant national characteristics. The growth of the integrated circularity index relates to an increase the aggregate index of economic sustainability in the post-crisis period. There is also a tendency to reduce the cross-country variability of economic resilience in recovery phase. The significance of the study in practice is showing the feasibility of integrating circular indicators into the system of macroeconomic monitoring and strategies for longterm structural stabilization within the European Union.

Modern climate challenges, growing energy consumption, and the need to reduce dependence on fossil resources make the use of renewable energy sources (further-RES) a key area of sustainable development. In Ukraine, the relevance of this problem is exacerbated by a combination of military threats, economic instability, and environmental risks. The purpose of the study is to identify the role of RES in green manufacturing and characterize their impact on environmental safety and energy efficiency, along with barriers and prospects for implementation. The methodological basis is an interdisciplinary approach that combines the analysis of international reports, scientific publications, statistical data, and scenario modeling. The results show that solar and wind energy are the most effective in rapidly reducing emissions, bioenergy contributes to the development of a circular economy, while hydro and geothermal energy ensure long-term stability of energy supply. It has been established that the introduction of RES in the agricultural sector can compensate for yield losses, reduce energy costs, and increase the adaptive capacity of production systems. In the industrial sector, the use of hybrid models with a combination of traditional and RES contributes to the modernization of energy-intensive industries and the reduction of greenhouse gas emissions. The practical significance of the study lies in the possibility of applying the results to the formation of state strategies for sustainable development, the creation of local energy clusters and support for energy independence. The identified legal, economic, and technological barriers point to the need for integrated approaches that combine innovative solutions, international cooperation, and educational initiatives. The findings confirm the novelty of the study in terms of a comprehensive assessment of the impact of RES on green production and emphasize the need for further interdisciplinary research based on analytical reports of international organizations and the integration of innovative solutions into national sustainable development strategies.

The article presents a comprehensive analysis of infrastructure recovery management as a strategic instrument for attracting investment in post-conflict regions. The relevance of the study is justified by the large-scale destruction of critical infrastructure, heightened security risks, and the need to stabilize economic processes and create conditions for long-term development. The study demonstrates that effective public management mechanisms play a decisive role in restoring investment attractiveness and enabling the transition from crisis response to sustainable modernization. The research assesses the effectiveness of strategic planning tools, digital management of infrastructure projects, public-private partnerships, risk-based approaches, and innovative reconstruction practices. Structural transformations in state infrastructure policy are analyzed, including the establishment of specialized recovery institutions, the introduction of digital monitoring platforms, increased regionalization of reconstruction processes, and the evolution of international assistance mechanisms. The methodological framework is based on systematic, comparative, and economic-analytical approaches, including scenario modeling, assessment of investment attractiveness,

The relevance of this study stems from the need to establish a sustainable system for the financing and logistical support of national resistance in the context of prolonged armed aggression, rising defence expenditure and limited budgetary resources. Contemporary security challenges require the development of new approaches to the mobilisation and management of resources capable of ensuring an adequate level of the state’s defence resilience. The aim of this study is to develop theoretical and methodological foundations and practical recommendations for improving the financing and logistical support of national resistance in Ukraine in the context of implementing the concept of modern finance. The subject of the study is the processes of financial and resource support for the components of national resistance. The study employs systemic, institutional, comparative and economic-statistical approaches, as well as methods of analysis and synthesis, econometric modelling, index analysis and expert assessment. To assess the relationship between a country’s economic potential and defence expenditure, a regression model was constructed, which demonstrated the existence of a strong statistical correlation between GDP per capita and expenditure on security and defence (R² = 0,9213). In addition, a comprehensive indicator of the effectiveness of funding for the security and defence sector has been developed, which takes into account the level of defence expenditure, its share of GDP, the structure of public expenditure, the Ministry of Defence of Ukraine’s share in the funding of the security and defence sector, and the rate of growth in the relevant expenditure. The study’s findings showed that Ukraine’s expenditure on security and defence rose from 191.7 billion UAH in 2018 to 3,830 billion UAH in 2025, whilst its share of public expenditure reached 70 per cent. The value of the integrated indicator rose from 0.0091 in 2021 to 0.9043 in 2025, indicating a significant increase in the priority and scale of defence funding. A model for multi-channel funding of national resistance has been proposed, combining resources from the state and local budgets, international aid, military bonds, public-private partnerships and the voluntary sector. The practical value of this work lies in the potential to use the proposed approaches to improve public policy in the areas of national resistance, medium-term budget planning and the digital management of defence resources.

The study addresses the growing importance of corporate social responsibility and environmentally responsible consumption in the context of global environmental challenges. Traditional marketing tools are becoming less effective, increasing the need for behavioral economics instruments, particularly “nudging”, which influences consumer choices through changes in decision-making architecture without limiting freedom of choice. The research aimed to substantiate the theoretical foundations of behavioral tools in green marketing and develop practical recommendations for their business implementation. The study applied systematic analysis, synthesis, comparative analysis, abstraction, and modeling. The findings demonstrate that effective green marketing depends on psychological, social, and cultural factors influencing consumer behavior. The most efficient approaches combine green defaults, informational and social nudges, and digital behavioral tools that personalize consumer engagement. The study also highlights ethical considerations, emphasizing transparency and preservation of consumer autonomy. Practical recommendations include adapting behavioral strategies to target audiences, integrating them with traditional marketing instruments, and using digital technologies for personalized communication. The results can support businesses in strengthening customer loyalty, competitiveness, and sustainable consumption practices.

The increasing complexity of construction enterprise management requires practical decision support tools that can improve management efficiency in real-world conditions. This paper presents an applied decision support system based on artificial intelligence for intelligent management of construction enterprises. The proposed system integrates machine learning models for forecasting key performance indicators, a fuzzy logic system for assessing management risks under uncertainty, and a multi-criteria decision-making method for selecting optimal management scenarios. The system was implemented and tested using construction enterprise data, including project schedules, financial indicators, and resource utilization parameters. Experimental results demonstrate that the proposed decision support system based on artificial intelligence improves forecasting accuracy and supports more effective management decisions, leading to a measurable reduction in cost overruns and project delays compared to traditional decision-making approaches. The results obtained confirm the practical applicability of intelligent information systems based on artificial intelligence to improve enterprise management in the construction industry.

The study is relevant due to the increasing complexity of public debt management amid macrofinancial instability, high borrowing costs, heightened fiscal risks, and the rapid digital transformation of public finances. In such conditions, state institutions need tools that allow them to process large volumes of debt, budget, macroeconomic, and market data more quickly, assess alternative scenarios, and identify threats to debt sustainability in a timely manner. The purpose of the article is to substantiate the opportunities, risks, and directions of using artificial intelligence to increase the analytical capacity of the public debt management system within the digital transformation of public finances. The object of the study is the public debt management system, and the subject is the theoretical, methodological, and applied aspects of integrating AI into the processes of forecasting, scenario analysis, risk management, and management decision support. The methodological basis of the study is a systematic approach, analysis and synthesis, comparative analysis, classification, grouping, scenario approach, risk analysis, scientific abstraction, and graphical modeling. As a result of the study, 12 areas of AI application in public debt management were systematized, namely: forecasting debt dynamics; assessing debt sustainability; modeling currency, interest, and refinancing risks; conducting scenario analysis; optimizing the structure of borrowings; and identifying anomalies in financial data. 8 groups of possibilities for using AI, 10 groups of risks, and corresponding safeguards to minimize them were also identified. An AI-based digital architecture was proposed for a debt management system, comprising 6 functional layers: data layer, analytical layer, decision–support layer, institutional layer, governance layer, and feedback layer. As a result, a conceptual model of AI integration into the public debt management system was developed, integrating the institutional framework, digital infrastructure, high-quality data, AI analytics, scenario calculations, management decisions, risk control, and debt sustainability monitoring. The practical value of the results lies in their potential use by public finance authorities to develop debt strategies, improve fiscal forecasting, enhance the transparency of analytical procedures, and inform approaches to the responsible use of AI in line with the human-in-the-loop principle.

This study develops a synergetic conceptual framework, integrating natural resource security, financial stability, and artificial intelligence within the economics of sustainable development. Drawing on synergetic theory and systems analysis, the research conceptualizes sustainability as a nonlinear adaptive process characterized by dynamic feedback loops and emergent systemic behavior. The proposed model demonstrates how natural resource dynamics, financial systems, and AI technologies form an interdependent triadic structure in which disturbances in one domain propagate across the entire system. Artificial intelligence is identified as a key mediating mechanism that enhances predictive capacity, optimizes resource allocation, and strengthens regulatory responsiveness, while simultaneously increasing system coupling and transition sensitivity. A formal synergy function is introduced to capture nonlinear interactions among ecological, financial, and technological subsystems. The study further identifies feedback loops and conditions for systemic instability, including potential tipping points in environmental and financial regimes. The findings suggest that sustainable development should be understood as AI-mediated synergetic process of co-evolution between natural and financial systems. The proposed framework contributes to interdisciplinary research on sustainability, complexity economics, and digital governance, offering implications for policy design in resource management, financial regulation, and AI governance.