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@article{190987,
        author = {Hardik Sharma and Bhanu Gaud and Ritam Mittal and Shivansh Mittal},
        title = {CARBON FOOTPRINT TRACKER : A SMART WEB APPLICATION FOR PERSONAL EMISSION MONITORING, PREDICTION, AND GAMIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6103-6108},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190987},
        abstract = {Climate change mitigation increasingly requires action at the individual level, as everyday lifestyle choices such as transportation, energy usage, and food consumption collectively contribute a substantial share of global carbon emissions [12]. Despite the availability of digital carbon footprint calculators, many existing tools provide static and survey-based estimations that fail to adapt to changing user behavior or support long-term sustainability planning [4]. This paper presents a Carbon Footprint Tracker, a smart full-stack web application designed to enable continuous personal emission monitoring, predictive analysis, and sustained user engagement.
The proposed system allows users to log routine activities related to mobility, household energy consumption, dietary habits, and waste generation. These activities are translated into carbon emission values using standardized and widely accepted emission factors to ensure calculation reliability [9]. Beyond real-time tracking, the platform incorporates a machine learning–based prediction model that analyzes historical activity patterns to estimate annual carbon emissions, allowing users to anticipate long-term environmental impact and evaluate the consequences of lifestyle changes [16]. To address the challenge of declining user motivation, the system integrates gamification mechanisms such as achievement badges, progress visualization, and leaderboards, which have been shown to encourage consistent participation in sustainability-focused applications [7].
The application is implemented using React for the frontend, Python-based FastAPI for backend services, and MongoDB/Firebase for scalable data management. By integrating predictive analytics with behavior-oriented design, the proposed system enhances environmental awareness and supports informed decision-making in personal sustainability management [5].},
        keywords = {Carbon Footprint Tracking, Sustainability Analytics, Predictive Insights, Gamification, Machine Learning},
        month = {January},
        }