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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67621

Carbon Footprint Estimator & Reducer

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67621

Volume:

10 |

9 |

Sep 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67621

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

JP

Jageti Padmavathy

Department of Computer Science and Engineering

G. Narayanamma Institute of Technology and Science

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S

S.Radhika

Department of Computer Science and Engineering

G. Narayanamma Institute of Technology and Science

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Abstract

The Carbon Footprint Estimator and Reducer is a complete online sustainable tool that has been built with the implementation of machine learning to facilitate easy and tailored assessment of carbon footprint. The tool offers a user interface, allowing users to input data in four diverse fields, namely Transportation, Electricity, Diet, and Waste. The users are provided an opportunity to calculate and evaluate carbon emission in a thorough and convenient manner. Machine learning models were trained in each domain to accurately calculate CO₂ emission based on user actions. The application provides total emissions, along with emissions per domain, by utilizing interactive Streamlit dashboards, thus making it very effective for monitoring and comparison purposes. Secondly, the application also predicts future carbon emissions by utilizing current usage patterns and even provides information regarding Scope 1, Scope 2, and Scope 3 emissions. Finally, for ease of usability, the application integrates chatbot using Gemini LLM API, which produces customized, human-readable instructions regarding the reduction of carbon emissions within the environment. The successful development of this application is one of the best ways to prove the combined efficiency of machine learning, data visualization, and AI-based instructions related to the encouragement of sustainable lifestyle habits.

KEYWORDS

Gemini LLM API

Cite This Article

Jageti Padmavathy, S.Radhika, "Carbon Footprint Estimator & Reducer," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-9, Sep 2026. https://doi.org/10.55041/IJSREM67621

Full Article/Conclusion

The Carbon Footprint Estimator and Reducer is a useful, data driven option to address one of the most pressing challenges of our time addressing our environmental footprint through a reliable carbon footprint estimation. This is a convenient and flexible tool that allows the individual and institutions to easily track, record, and reduce greenhouse gas emissions, while saving time and effort. The tool could be useful for the individual as well as the institutional, collecting data from machine learning algorithms, interactive data data analytics, visualization, and providing estimates of carbon emissions from a variety of activities with precision. The dashboard-based interface of the platform increases usability through providing real-time insights, trends, and tracking of progress. In short, the project enhances environmental awareness and users to take measurable action towards sustainability. The use of IoT devices and intelligent sensors for collecting real-time data will allow users to monitor their energy, water, and transportation consumption, which assists in establishing an accurate emissions profile of the area in which they live. The emissions data collected will allow an expansion of the emissions dataset through the addition of regional and industry-related data, while simultaneously allowing them to use methods such as carbon credits or purchasing renewable energy to offset their emissions. Machine learning will provide the user with the ability to forecast future emissions and develop sustainability strategies. Through the use of gamification such as badges and achievements, users can be motivated to engage in environmentally friendly behaviour.

References

Ø  Ahmed, S., Rahman, T., & Zhou, L. “A Review of Machine Learning and Statistical Approaches for Carbon Emission Prediction,”.Environmental Data Analytics Journal,2022. Ø  Brown, R., & Li, Y. “Temporal Deep Learning Models for Carbon Emission Forecasting Using LSTM and Transformer Networks,” Journal of Smart Energy Systems, 2024. Ø  Chen, Y., & Kumar, R. “Carbon Footprint Assessment Across Regional Energy Systems Using SVM and Decision Trees,” International Journal of Sustainable Energy Analytics, 2021. Ø  Davis, M., Ortega, S., & Lin, P. “Synthetic Data Generation and Fairness Evaluation for Carbon Emission Forecasting,” Journal of Environmental Informatics Research, 2022. Ø  Garcia, R., Lopez, D., & Martins, A. “Interpretable Machine Learning Models for National and Regional Carbon Emission Forecasting,” Sustainability Modeling and Policy Review, 2021. Ø  Johnson, A., & Patel, R. “Behavior-Aware Machine Learning for Real-Time CarbonFootprint Estimation,” Journal of Smart Environmental Systems, 2020. Ø  Lee, H., Park, J., & Silva, M. “ML-Based Prediction of Urban Energy Use and Transportation Emissions Using Random Forest and Gradient Boosting,” Urban Computing and Sustainability Journal, 2020. Ø  Nguyen, T., Roy, S., & Abbas, K. “Transfer Learning for Carbon Emission Prediction in Data-Limited Regions,” International Journal of Green AI Systems, 2023. Ø  Smith, J., Carter, B., & Wilson, M. “Machine Learning Models for Carbon Emission Estimation Across Lifestyle Sectors,” Journal of Climate Informatics, 2019. Ø  Tanaka, R., & Roy, P. “Explainable AI Techniques for Carbon Emission Modeling Using SHAP and Rule-Based Systems,” Environmental AI Transparency Review, 2023.

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Publication Date

9/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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