This project is a web application focused on visualizing and analyzing the Environmental Kuznets Curve (EKC) for various countries. The EKC hypothesis suggests that as an economy develops, environmental degradation initially increases and then decreases after a certain level of income per capita is reached.
Key Features:
- Data Collection: Utilizes web scraping to gather real-world data on GDP and environmental indicators (e.g., CO₂ emissions, air pollution) from online databases.
- Machine Learning Predictions: Employs a machine learning model to fill in missing data and make future predictions for selected indicators.
- Interactive Visualization: Provides an intuitive web interface built with HTML, CSS, and JavaScript, allowing users to view EKC trends and projections for specific countries.
This project serves as an educational tool to understand the EKC concept and explore environmental trends across various economic stages.
This project is developed as part of the initiative: "Gender, Digitalization, Green: Ensuring a Sustainable Future for all in Europe" Ref. Project: 2023-1-RO01- KA220-HED-000154433, Partnership: Universitatea de Stiinte Agricole si Medicina Veterinara, Bucuresti, Romania, Universitatea Nationala de Stiinta si Tehnologie POLITEHNICA București, Romania, Universitat Autonoma de Barcelona, Espana, Universidade do Porto, República Portuguesa, Uzhgorodskyi Nacionalnyi Universitet, Ukraina.