This study investigates the role of environmental regulations, renewable energy, and the validity of the load capacity curve (LCC) hypothesis. The natural environment offers a range of ecosystem services; however, it is important to recognize that these services have finite limits, and overuse can result in a biocapacity deficit. To restore biocapacity, it is essential to prioritize renewable energy, enforce stricter environmental taxation, and closely monitor trade flows. This paper first assesses Romania's LCC hypothesis by considering the impact of environmental taxes, renewable energy, and trade. Utilizing data from the first quarter of 1994 to the fourth quarter of 2022, the study employs method of moments quantile regression and quantile regression techniques to derive its empirical findings. The results confirm the validity of the LCC hypothesis and highlight the significant influence of trade and renewable energy on the load capacity factor. These findings offer valuable insights for Romanian policymakers, suggesting that adopting renewable energy and environmental taxes can mitigate trade's ecological and socioeconomic impacts without hindering economic growth and the pursuit of sustainable development goals. The authors also propose new policy recommendations to align sustainable energy use with economic objectives.

Water pollution has become a global problem and its impact on the health of the human population is growing day by day. This study aims to assess the pollution of the Cibin River (Romania) by a physicochemical analysis. Water samples have been collected from four locations along the Cibin River over a period of 12 months. At this time, there are several commonly used Multiple-Criteria Decision Analysis (MCDA) methods for the assessing the impact of pollutants on the environment. In this research, we used the Analytic Hierarchy Process (AHP) multi-criteria method to evaluate each sampling station’s physicochemical parameters. The significant results place the river in the first (sampling stations 1, 2 and 3) and second (sampling stations 4) water quality classes. Another significant result of this work is that the research using GIS software allowed an integrated automatic data collection system and displays interactive results.

The pollution caused by road traffic is a significant problem for society in the 21st century. Climate change and mortality among the population, are increasingly influenced by increasing traffic in urban agglomerations. The present study aims at the multi-criteria analysis of the pollution caused by traffic at several busy intersections in the city of Sibiu, Romania. Classification of intersections was done using the Synchro Studio with Warrants software. Air pollutants, noise, and the number of vehicles in the traffic were taken into account. To analyze these criteria as a whole, the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) methods were used. A comparative study has been undertaken on the results obtained by applying the two multicriteria methods. The paper aims to provide solutions for the eco-economy of the future.

Agriculture is a business sector ideally suited for the application of Geographic Information Systems (GIS) because it is natural resource based, requires the movement, distribution, and/or utilization of large quantities of products, goods, and services, and is increasingly required to record details of its business operations from the field to the marketplace. Nearly all agricultural data has some form of spatial component, and a GIS allows you to visualize information that might otherwise be difficult to interpret. Environment has a major impact on agriculture. In this paper we presented how GIS software can be used to analyze risk factors that influence agricultural production naturally. Natural risk factors were taken into account are: land degradation, flooding, humidity, action on farmland of the wildlife. The conclusions drawn from this paper using GIS allows the adoption of important measures on a short or long time to reduce natural risk factors on agricultural production. The advantage of this model is possibility to bee extended to national, regional and global area.

The aim of this paper is to analyze the causal relationship between inflation and global supply chain pressure in the Eurozone. In contrast to the full-sample causality method, this paper utilizes the bootstrap subsample rolling window causality method to account for structural changes. Initially, the computed vector autoregressive models demonstrate that the short-term relationship between inflation and global supply chain pressure is unstable. The dynamic causal relationship is reexamined in the subsample rolling window causality test using a time-varying method (RB bootstrap-based modified-LR causality test). The results indicate that inflation is influenced by the expansion of global supply chain pressure in a variety of sub-periods, with both positive and negative effects. Conversely, inflation fluctuations increase the uncertainty of the Global Supply Chain Pressure Index. The novelty of the findings is that they illustrate bidirectional causal relationships between the two variables, which is in contrast to the existing body of empirical research that does not support the direction of causality. The implications of these findings emphasize the need to implement appropriate monetary policy measures in order to mitigate the inflationary consequences of disruptions in the global supply chain and to ensure a more stable supply chain network.

This chapter explores the analytical junctions of Artificial Intelligence (AI) and Sustainable Development Goals (SDGs). The chapter highlights the important need for decisive actions in the face of a triple planetary crisis encompassing climate change, pollution, and biodiversity loss. It encourages for a commitment to leveraging AI to fulfill its promises, particularly in promoting digital equality, advancing the SDGs, and connecting AI for the greater good. The chapter explores the potential of AI, together with other high-potential technologies like virtual reality and digital twins, in driving sustainable operations and addressing pressing challenges. The discussion extends to the role of AI in reconstruction efforts for countries facing destruction, emphasizing the importance of systemic changes and practical applications of AI to optimize outcomes for millions of individuals. Through a nuanced understanding of the complex interdependencies between AI, sustainable development, and environmental supervision, the document highlights the transformative potential of AI in shaping a more sustainable and equitable future.