As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence. The main goal of the research was to build, train and validate an artificial neural network that is able to forecast the following year’s value of economic sentiment using the present values of the other indicators. Research on predicting European Economic Sentiment Indicator (ESI) using artificial neural networks is a starting point, with work on this subject almost inexistent, the reason being mainly that ESI is a composite of five sectoral confidence indicators and is not thought to be an emotional response to the interaction of the entrepreneurial population with different economic indicators. The authors investigated, without involving a direct mathematical interaction among the indicators involved, predicting ESI based on a cognitive response. Considering the aim of the research, the method used was simulation with an artificial neural network and a feedforward network (structure 4-9-6-1) and a backward propagation instruction algorithm was built. The data used are euro area values (for 19 countries only—EA19) recorded between 1999 and 2016, with Eurostat as the European Commission’s statistical data website. To validate the results, the authors imposed the following targets: the result of the neural network training error is less than 5% and the prediction verification error is less than 10%. The research outcomes resulted in a training error (after 30,878 iterations) of less than 0.099% and a predictive check error of 2.02%, which resulted in the conclusion of accurate training and an efficient prediction. AI and artificial neural networks, are modeling and simulation methods that can yield results of nonlinear problems that cover, for example, human decisions based on human cognitive processes as a result of previous experiences. ANN copies the structure and functioning of the biological brain, having the advantage through learning and coaching processes (biological cognitive), to copy/predict the results of the thinking process and, thus, the process of choice by the biological brain. The importance of the present paper and its results stems from the authors’ desire to use and popularize modern methods of predicting the different macroeconomic indices that influence the behavior of entrepreneurs and therefore the decisions of these entrepreneurs based on cognitive response more than considering linear mathematical functions that cannot correctly understand and anticipate financial crises or economic convulsions. Using methods such as AI, we can anticipate micro- and macroeconomic developments, and therefore react in the direction of diminishing their negative effects for companies as well as the national economy or European economy.

The automotive industry’s use of aluminum alloys continues to rise, driven by efforts to reduce vehicle weight—and thus fuel consumption—amid growing demand for larger vehicles such as SUVs, as well as the accelerating shift to electric vehicles and the expanding global vehicle fleet. These trends create major challenges for the aluminum sector. This paper provides a narrative literature review of available and published data, primarily from the period 2020–2025, examining new trends, challenges and opportunities regarding the implementation of recycled aluminum as a substitute for primary aluminum in the European automotive industry. The goal is to develop a discussion based on the answer to the following three issues: (1) What opportunities exist for increasing the production of recycled aluminum, given the imperative to conserve diminishing raw materials required for primary aluminum production? (2) What methods could enhance the obtaining of recycled aluminum over primary aluminum? (3) How might the technological barriers that hinder the wider use of recycled aluminum be overcome? This review finds that recycled aluminum availability in the EU automotive sector is improving due to rising demand for recycled material over primary aluminum—supported by a steadily growing scrap supply—alongside the development of advanced recycling strategies capable of producing high-purity recycled alloys.

This study presents a hybrid modeling framework aimed at enhancing climate mitigation strategies by evaluating the predictive power of sustainability indicators using both statistical analysis—correlation metrics, regression modeling, distribution tests—and artificial neural networks (ANNs). The analysis centers on variables critical to climate outcomes, including renewable energy use in transport and electricity, greenhouse gas emissions from production, and aggregated target completion values. The findings identify renewable energy usage in transport as the primary predictor of improved performance in the Sustainable Development Report (SDR), followed by overall target completeness, electricity-based renewables, and production-related emissions. Multidimensional interaction analyses highlight a synergetic link between transport renewables and target achievement, underscoring their strategic relevance for climate mitigation efforts. The ANN models demonstrate high predictive accuracy and minimal error, affirming the model’s suitability for scenario-based climate forecasting. Results offer actionable intelligence for policymakers and climate stakeholders to optimize resource allocation and accelerate low-carbon transitions. The study acknowledges limitations, namely, the relatively small dataset and EU-centric analysis, and recommends future extensions to more geographically diverse datasets and the incorporation of advanced econometric techniques and AI frameworks to improve generalizability and predictive potency.

Understanding the factors that influence the Consumption Footprint (CF) is essential for advancing sustainable development within the European Union. This study investigates the most impactful indicators affecting CF, aligning the analysis with the 17 Sustainable Development Goals (SDGs), which are grouped into five thematic clusters: economic conditions, globalization, health, environmental awareness, and cultural factors. To identify key drivers, the research employs a dual-method approach: Graphical representations and correlation analyses and machine learning via Artificial Neural Networks (ANNs), supported by statistical analysis using non-parametric tests. Data from Romania (2012–2023) were used to evaluate the influence of variables such as Gross Domestic Product (GDP), Price Level Indices (CPI), Unemployment Rate (UNE), and Circular Material Use Rate (CMU) on CF. The results reveal that GDP and CPI are the most influential variables, together accounting for over 64% of the impact on CF, followed by UNE and CMU. The study concludes that economic indicators play a dominant role in shaping consumption-related environmental impact. The proposed framework is replicable and adaptable, offering valuable insights for policymakers and researchers aiming to accelerate progress toward EU sustainability targets.