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@article{193974,
        author = {Karanam Praneethanjanee and Muthoju Harshini and Neerudi Sravani and Dr. D. Radhika},
        title = {Carbon-Wise: Toward Intelligent Estimation of Personal Carbon Footprints},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8158-8164},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193974},
        abstract = {Climate change mitigation requires effective tools that enable individuals to measure and understand their personal carbon footprint. While large industrial emissions are widely monitored, emissions generated through daily lifestyle activities remain less visible to individuals. This paper presents Carbon-Wise, an AI-powered carbon footprint tracking system designed to estimate and analyze individual carbon emissions based on lifestyle patterns. The proposed system integrates emission-factor based carbon estimation with machine learning regression models to predict monthly carbon emissions. A hybrid dataset combining survey responses and synthetic lifestyle data was constructed to improve model generalization. The dataset includes features related to electricity consumption, transportation habits, dietary patterns, household size, flight frequency, and renewable energy usage. Two regression models Linear Regression and Random Forest were trained and evaluated using standard metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). Experimental results demonstrate that the proposed approach achieves reliable prediction performance while providing interpretable insights into the lifestyle factors contributing to carbon emissions. The Carbon-Wise platform further provides interactive visual analytics and sustainability recommendations to encourage environmentally responsible behavior. The system demonstrates how machine learning and data-driven insights can support individual-level climate awareness and sustainable lifestyle choices.},
        keywords = {Carbon footprint prediction, machine learning, sustainability analytics, emission estimation, regression modeling, environmental monitoring.},
        month = {March},
        }