Assessing the Impact of Socioeconomic and Environmental Indicators on the Consumption Footprint Using Statistical and Neural Network Analyses

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.