International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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EcoPredictAI: An Intelligent Web-Based Carbon Footprint Tracker with Predictive Analytics and AI-Driven Recommendations

Author(s) Dr. JAYANTHI KANNAN KANNAN, Mr. Ujjwal Kumar
Country India
Abstract EcoPredict AI is an intelligent web-based platform developed using the MongoDB, Express.js, React, and Node.js stack to help individuals track, analyze, and reduce their carbon footprint. The system allows users to input daily lifestyle activities such as transportation usage, electricity consumption, food habits, and waste generation. Based on these inputs, the platform calculates the user’s carbon emissions and provides a clear overview of their environmental impact. The application integrates the OpenAI API to generate intelligent insights and personalized recommendations that help users adopt more sustainable habits. Using historical user data, the system can also analyse emission patterns and forecast future carbon footprints through predictive models and data analytics techniques. The platform presents these insights through an interactive dashboard with visual charts and trend graphs, enabling users to monitor their daily, weekly, and monthly CO₂ emissions. EcoPredict AI aims to promote environmental awareness by transforming complex emission data into simple, actionable insights. EcoPredict AI addresses this gap by providing an intelligent web‑based platform built on the MERN stack (MongoDB, Express.js, React, Node.js) that enables users to track, analyze, and reduce their carbon footprint. Users input lifestyle data across categories such as transportation, electricity usage, dietary habits, and waste generation. The system calculates CO₂ emissions using established conversion factors and presents the results through interactive dashboards featuring visual charts and trend graphs. By leveraging the OpenAI API, EcoPredictAI generates personalized, actionable recommendations to help users adopt greener habits. Additionally, historical data is used to forecast future emissions through predictive modeling, empowering users to anticipate their environmental impact and set reduction goals. The platform combines artificial intelligence, data analytics, and a user‑centric interface to transform complex emissions data into simple, engaging insights. This work demonstrates that a well‑integrated AI‑powered tracking system can significantly enhance environmental awareness and encourage sustainable behavior change.
Keywords Carbon Footprint, MERN Stack, Predictive Analytics, OpenAI API, Sustainability, AI Recommendations, Environmental Awareness
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-05
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E-ISSN 2582-2160
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