Lyazzat Sembiyeva

This paper conducts an audit of the definitional layer of the Polish Atomic Law through the lens of legislative drafting principles and the terminology of the Directive 2013/59/Euratom. It demonstrates that the unusually extensive glossary of the Atomic Law is structurally fragmented across the general definitions in Article 3 and multiple chapter-specific glossaries, including cases where technical concepts are defined only for specified chapters of this Act while operating throughout its normative framework. The analysis further shows that parallel “general” and “special” definitions within a single statute may destabilize interpretative reconstruction. The article also identifies a multilingual defect in the Polish-language version of the Directive concerning the clearance/exemption distinction, and highlights additional scope-sensitive divergences in Polish transposition (natural radiation; dose limit). These findings underscore the regulatory costs of definitional inconsistency for legal certainty and the uniform application of Euratom standards.

In the face of accelerating technological change and growing complexity in infrastructure development, traditional approaches to strategy formation and control have become inadequate. This paper examines the role of strategic audit in assessing and enhancing the effectiveness of strategies for implementing infrastructure projects and programs under conditions of uncertainty and turbulence. The research aims to clarify the conceptual link between organizational development strategy and strategic audit in the infrastructure domain and to propose an audit framework capable of evaluating strategic responsiveness, adequacy of organizational and economic elements, and alignment with external factors. Methodologically, the study employs a combination of literature review, systematization, diagnostic interviews, PEST analysis, and SWOT techniques, together with lessons drawn from the IPMA Delta model and recent innovations such as simulation-based control in complex projects. The results include a diagnostic checklist, a set of improved audit procedures (formalization, scenario analysis, digital analytics) and a refined SWOT-based assessment tailored to infrastructure contexts. It is shown that the audit of strategy is not a mere verification exercise but a continuous adaptive process that helps management teams detect strategic misalignments, strengthen institutional resilience, and embed proactive learning. The paper concludes with recommendations for integrating digital tools (AI, big data), building strategic competencies, and aligning national infrastructure agendas with project-level audit practices. Future research should test the proposed audit framework empirically in diverse geographic and sectoral settings. Practical significance is demonstrated using the example of the Western Europe-Western China highway project, showing how strategic audit enables proactive management adjustment and resilience strengthening.

The global shift towards sustainable development and low-carbon growth has intensified the need for efficient management of natural resources. This study proposes an integrated economic assessment framework to evaluate how ESG (Environmental, Social, and Governance) integration and circular economy strategies influence resource productivity and long-term economic performance. The research focuses on the water–energy–land nexus as a critical driver of global economic systems. Using a combination of multi-criteria decision analysis (AHP/TOPSIS), material flow analysis (MFA), life-cycle assessment (LCA), and panel econometric modeling on a broad dataset of countries (2018–2023), we examine the relationship between resource efficiency, ESG adoption, and economic competitiveness. The results indicate that circular business models and strong ESG practices significantly reduce resource intensity, enhance total factor productivity, and strengthen economic resilience. Scenario modeling demonstrates that transitioning from linear to circular resource flows can yield substantial economic and ecological benefits, including a ~1–3% rise in GDP and a ~15–20% drop in resource intensity under a high-circularity scenario. These findings provide actionable insights for policymakers and businesses, emphasizing that sustainable resource governance is not only an environmental necessity but also a key driver of global economic transformation.

Forecasting the stock market, particularly the S&P 500 index, is an essential yet challenging task due to inherent volatility, nonlinearity, and structural instability in financial data. This study investigates the effectiveness of different predictive models. It examines classical statistical approaches such as ARIMA, VECM, ETS, and Holt-Winters. It also evaluates machine learning algorithms including Random Forests, XGBoost, LightGBM, SVR, k-NN, and MLP. Additionally, it analyzes deep learning architectures like LSTM, GRU, and ARIMA-LSTM. The study further includes adaptive neuro-fuzzy inference systems. All models use financial, macroeconomic, and technical indicators. These indicators were collected from reputable sources between May 2020 and May 2025. The results indicate a significant enhancement in predictive accuracy when incorporating macroeconomic and technical indicators, validating the primary hypothesis. Among evaluated models, ANFIS demonstrated superior forecasting capability, effectively capturing market complexities and uncertainties, thus supporting the second hypothesis. Furthermore, feature selection and dimensionality reduction techniques considerably improved model accuracy and robustness, confirming the third hypothesis. However, limitations such as data non-stationarity, structural breaks, and inherent market noise constrained the forecasting precision. This research underscores the potential of advanced and hybrid predictive methodologies, providing valuable insights for stakeholders navigating the dynamic landscape of financial markets.

In the context of the increasing dependence of most regions of Kazakhstan on the republican budget, the problem of the effectiveness of interbudgetary relations is acquiring particular significance. There is a trend toward growth in the volume of subventions, an increase in the number of subsidized regions, and a decrease in the share of donor regions, which indicates a decline in the financial independence of local authorities. Consequently, the role of state audit bodies as a tool for enhancing the effectiveness of budget management at the regional level is becoming more prominent. The study employs an integrated approach, including analysis of the regulatory framework, comparative analysis of regional budget indicators, mathematical modeling of interbudget flows, construction of dendrograms, and correlation analysis. Data on transfer volumes in Kazakhstan's regions for 2023, along with visualized data from official statistics, serve as the empirical basis. Findings reveal that in 2023, only two regions transferred more to the republican budget than they received, while the remaining 18 regions were subsidized. The transfer ratio indicators demonstrated significant inequality, with some ratios exceeding 500-900 times. The share of subsidies in local budget revenues reached 37%, indicating a high level of dependence. Weak links were identified between the effectiveness of audit activities and the distribution of budget funds, particularly in subsidized regions. The current model of interbudgetary relations in Kazakhstan requires reform. Strengthening the role of the state audit should be accompanied by a systemic revision of transfer policies aimed at stimulating regions to develop their own revenue bases and adopt responsible financial management. The study's results can inform proposals for optimizing interbudgetary regulation mechanisms, improving legislation related to state audits, and developing effective strategies to enhance budget sustainability at the local level.

This article examines the modeling of sustainable development in transport logistics, focusing on the impact of climate factors, changing weather conditions, and digitalization processes. The study analyzes the complex influence of adverse weather phenomena, such as fog, rain, snow, extreme temperatures, and strong winds, whose frequency and intensity are increasing due to climate change, on the efficiency, safety, and reliability of transport systems across all modes except pipelines. Special attention is paid to the integration of weather-resilient sensor technologies, including LiDAR, thermal imaging, and advanced monitoring systems, to strengthen infrastructure resilience and ensure uninterrupted transport operations under environmental stress. The methodological framework combines comparative analytical methods with economic–mathematical modeling, particularly Leontief’s input–output model, to evaluate the mutual influence between the transport sector and sustainable economic growth within an interconnected ecosystem of economic and technological factors. The findings confirm that data-driven management strategies, the digital transformation of logistics, and the strengthening of centralized hubs contribute significantly to increasing the resilience and flexibility of transport systems, mitigating the negative economic impacts of climate risks, and promoting long-term sustainable development. Practical recommendations are proposed to optimize freight flows, adapt infrastructure to changing weather risks, and support the integration of innovative digital technologies as part of an evolving ecosystem.

In the context of a sustainable transition to a "green" economy, the modernization of fiscal instruments adapted to the specific features of green bonds and investments has become increasingly important. The objective of this study is to determine how digital technologies are transforming tax administration in the field of green finance in Kazakhstan. The research employs comparative, structural, and regulatory analysis, along with methods for evaluating digital performance indicators such as the implementation of cash register machines (CRMs), electronic invoices (e-invoices), and biometric identification. The study provides an empirical assessment of digital initiatives introduced by the Ministry of Finance of the Republic of Kazakhstan and examines their impact on the transparency of green taxation mechanisms. Based on the analysis of digital solutions in tax administration and the current state of the green securities market, policy recommendations have been formulated to adapt the tax system. It has been established that digitalization reduces risks, simplifies fiscal procedures, increases the efficiency of control and stimulates investments, especially in such an inactive segment as green bonds. Measures are proposed to strengthen the automation of tax audits and to incorporate ESG reporting into the tax framework. The results confirm the necessity of developing a distinct approach to tax regulation within the green economy sector.

In the face of growing climate challenges and global economic changes, Kazakhstan is actively changing its economy in accordance with international standards of sustainable development. The article discusses the main directions of the transition to a «green» economy, including renewable energy sources, environmentally friendly technologies, the development of sustainable agriculture and improved water management. As part of the analysis, attention was paid to the role of ESG in modernizing the economy of Kazakhstan, increasing business social responsibility and improving environmental standards. The study showed that the transition to a «green» economy requires not only technological changes, but also a reorganization of corporate governance and public policy. The article emphasizes the importance of joint efforts of the state, business and society to achieve sustainable development of the country based on the principles of ESG.