This study investigates how cloud accounting technologies contribute to the sustainable transformation of business management in Romania. It aims to assess the relationship between digitalization and sustainability by examining how cloud-based systems enhance financial transparency, operational efficiency, and the integration of Environmental, Social, and Governance (ESG) principles in accounting practices. The research applies a multiutility global method (MUGM) framework to evaluate sustainability-oriented accounting practices across multiple economic sectors, including IT, automotive, energy, and food industries. Data were collected through structured surveys and expert validation to determine sector-specific performance scores. The analysis integrates both subjective assessments and objective indicators, such as reductions in paper usage, cost efficiency, and reporting timeliness-to evaluate sustainability outcomes and potential biases. The results reveal significant sectoral variation in the adoption and sustainability impact of cloud accounting. The IT and automotive sectors lead in digital integration and ESG-oriented financial reporting, while the energy and food industries demonstrate moderate progress constrained by regulatory and investment limitations. Cloud technologies are shown to facilitate improved ESG data management, enhance corporate accountability, and support the EU twin transition, the simultaneous pursuit of digitalization and sustainability. This research extends sustainability accounting literature by positioning cloud computing as a driver of responsible corporate governance and ESG transparency. It bridges the gap between digital transformation and sustainability by demonstrating how intelligent technologies can operationalize sustainability objectives in financial management and reporting.

The dual transition, green and digital, has become the strategic axis of the European Union, but quantitative evidence on the extent to which the adoption of digital technologies and artificial intelligence effectively supports the decoupling of economic growth from material consumption remains fragmented. Our study examines this relationship for the 27 Member States of the European Union over the period 2015–2024, using a balanced panel constructed entirely from verifiable official secondary data, without any interpolated values. The dependent variable is resource productivity, expressed as the ratio between gross domestic product and domestic material consumption. The results of the panel data analysis show that the variables are first-order integrated and cointegrated. The fixed-effects model indicates a positive and significant effect of digital adoption on resource productivity, with human capital being the most robust determinant, and research intensity also exerting a positive effect. An analysis of the recent series of AI adoption (2021, 2023, 2024) highlights a positive and significant association with resource productivity. The findings support the idea that digitalization and AI can be complementary to sustainability objectives, but not a substitute for investment in human capital and institutional quality. The study features a two-component architecture: a balanced panel (2015–2024) for digital adoption and an exploratory, repeated cross-sectional analysis for the recent series on artificial intelligence adoption.

Ensuring the financial stability of the regions in the context of global economic instability and changes in the modernization of the economy of Kazakhstan has become the main factor of sustainable development. The objective of the study is to develop a methodological approach to assessing and forecasting the financial sustainability of regional development in the context of economic modernization using the Karaganda region as a case study and to develop practical recommendations. The application of methods of analysis and synthesis, comparative and structural analysis, and dynamic analysis of socio-economic indicators made it possible to use econometric modeling methods to forecast GRP. Within the framework of a unified statistical and econometric approach, a linear trend model of the GRP dynamics of the Karaganda region was created based on official statistical data for 2010-2024, estimated by the least square’s method. The resulting model was used to generate point and interval forecast estimates of GRP for 2025-2027. The forecast results indicate a continued, stable, upward trend in regional economic development; even the lower bound of the 95% confidence interval reflects positive dynamics for the indicator. Furthermore, as the forecast horizon increases, the width of the confidence interval widens, indicating an increase in uncertainty and confirming the scenario-based, predictable nature of the forecast estimate with a limited amount of initial data. The research results can be used by state and local government bodies to adjust territorial development strategies, formulate investment policies, and improve the efficiency of budgetary resource use. Keywords: modernization, financial stability, strategic management, regional economy, gross regional product, forecasting.