International Journal For Multidisciplinary Research

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

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Data-Driven Prediction of Vehicular CO2 Emissions: A Linear Regression Approach for Sustainable Transportation

Author(s) Ms. Chithanuru Jayasree, Mr. Donepudi Chaitanya, Ms. Rondla Vaishnavi Reddy
Country India
Abstract The transportation sector’s carbon dioxide (CO2) emission reduction challenge needs predictive tools that are both accessible and accurate. Traditional ways, either involving expen- sive physical experiments or implementation of sophisticated real- time sensors, typically aren’t available to individual consumers or smaller manufacturers. This research paper proposes a novel, ef- ficient, and highly interpretable system for forecasting a vehicle’s CO2 output based only on easily available technical specifications, like engine size, number of cylinders, fuel type, and consumption metrics. We employ a Linear Regression model which is simple but yet showing a tremendous performance in indicating the strong quantifiable connection that exists between these features and the emissions levels. Our strategy is to cost-effectiveness, speed, and model transparency rather than very high-complexity deep learning solutions. The system is made available through a contemporary web application where users obtain instant, data- supported predictions and tailored advice as an investment in sustainable vehicle utilization. The developed model presents a dependable option to intricate simulations, thereby increasing environmental impact analysis to a larger public audience.
Keywords CO2 Emission Prediction, Linear Regression, Vehicle Attributes, Predictive Modeling, Sustainable Transporta- tion, Model Interpretability.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-25
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.65691
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E-ISSN 2582-2160
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