This research emphasizes the analysis and assessment of the environmental impact in the construction sector. It aims to propose a model for analyzing the management of environmental resources in the building process. This specific sector presents a significant potential risk of negatively affecting the environment, through pollution, energy consumption, and waste generation. Environmental responsibility involves analyzing the environmental impact and implementing effective solutions for an adequate governance of the environmental resources. Various life cycle assessment methods are employed to design a new model of management of environmental resources, starting from the factors impacting the environment. The resulting model takes these factors into account and carries several advantages from an organizational point of view. One novel result is that the model can be applied at a microeconomic level as well as at a macroeconomic level. By implementing this model, the preconditions for reconfiguring operational conditions and processes with significant impact on resource consumption within construction projects can be created at a company level. As such, the objective for the construction sector is to manage the use of resources efficiently and, at the same time, to restore the areas possibly affected by the specific operations.

Nowadays, many businesses recognize the importance of digitization as a strategic advantage to maintain competitiveness. As a result, current research aims to investigate three significant factors that influence digital transformation: digital innovation (DI), digital orientation (DO), and the accounting information system (AIS). These factors are supposed to impact the financial success and performance of firms. The objective of this study is to examine the positive association between digital orientation and financial performance (FP) and to explore how the AIS mediates this relationship. Additionally, this paper investigates the moderating role of digital innovation in the relationship between digital orientation and financial performance. To collect data, a quantitative method was employed using a random sampling technique. Data were collected via a questionnaire survey from small and medium-sized enterprises (SMEs) in China, with a sample size of 550. Structural equation modeling (SEM) and a bootstrapping technique with 5000 iterations were used to test the study hypotheses. The findings of this study indicate a positive link between digital orientation and the financial performance of organizations. Furthermore, the results confirm that the AIS acts as a mediator between digital orientation and financial performance. The outcomes also demonstrate that digital innovation plays a moderating role in the relationship between DO and financial performance. This paper contributes to the existing literature on digital transformation by providing a deeper understanding of the aforementioned antecedents of the digital revolution (DO, AIS, and financial performance) and how they should be integrated within digitalization settings to achieve financial performance.

In a society where the market value of a company is given in proportion of 75-85% of intangible assets value, we can say that the current accounting does not provide sufficient relevant information for a company in order to take long-term decisions. For Skandia, the company's market value is given by two components: financial capital and intellectual capital. Accounting for intellectual capital is not exactly easy to achieve, both because of the assessment and viability in the accounting process i n view of current legislation. This article aims to show what are hidden intangible assets (intellectual capital), what is their place in the classification of intangible assets, what are the components of hidden intangible assets according to different existing reference models and also the possible solutions such as submission of additional financial situations

The current development of multimodal transport networks focuses on the realization of intelligent transport systems (ITS) to manage the prediction of traffic congestion and urban mobility of vehicles and passengers so that alternative routes can be recommended for transport, especially the use of public passenger transport, to achieve sustainable transport. In the article, we propose an algorithm and a methodology for solving multidimensional traffic congestion objectives, especially for intersections, based on combining machine learning with the templates method—the golden template algorithm with the multiagent game theory. Intersections are modeled as independent players who had to reach an agreement using Nash negotiation. The obtained results showed that the Nash negotiation with multiagents and the golden template modeling have superior results to the model predictive control (MPC) algorithm, improving travel time, the length of traffic queues, the efficiency of travel flows in an unknown and dynamic environment, and the coordination of the agents’ actions and decision making. The proposed algorithm can be used in planning public passenger transport on alternative routes and in ITS management decision making.

This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of improving the accuracy of expenditure forecasting in the budget process and supporting financial accounting decisions in public institutions. Using historical and statistical data from a group of public institutions, the research applies both univariate and multivariate forecasting strategies, evaluated with performance metrics. The research focuses on the development of an innovative forecasting model based on AI, using historical and statistical data from public sources and case studies of local public institutions to transform them into smart cities. The selected AI algorithms include artificial neural networks, support vector machines, and deep learning models, implemented and evaluated using Python v3.14. The research results show that AI can significantly improve the accuracy of budget forecasts compared to traditional methods, such as linear regression and econometric models. The use of AI contributes to increasing transparency and accountability in the management of public funds, providing more detailed and well-founded forecasts.