Predicting Carbon Footprint of Transportation in Lagos State Using Ensemble Machine Learning Model

Domaine:

climatemobility

Type de record:

paper

Créateur:

I.US.AL.

Éditeur:

Hev

Hôte:

The exponential growth of vehicle ownership in Lagos state is one of the indicators of the gargantuan amount of carbon footprint pump into the atmosphere thereby making the estimation of the carbon footprint imperative. This study examined and predicted the carbon footprint produced by private and commercial cars in the state using an Ensemble machine learning prediction model. The predictor variables considered in this study include the historical data of the registered private and commercial cars, internally generated revenue and the population of the state from 2010 to 2024 which were obtained from the National Bureau of Statistics and World Bank. Linear regression, polynomial regression, support vector regression, ARIMA and Monte Carlo simulation were used to train the historical data using Python programming to obtain the Ensemble prediction model. The results obtained from Ensemble prediction model showed that the total carbon footprint produced by the registered cars in Lagos to be 384.47 MtCO2e in 2025 and 661.07 MtCO2e in 2050. This result shows an increase of about 71.9% carbon footprint from 2025 to 2050. The result is alarming and suggests that transportation in Lagos and other urban centres is a major source of carbon emissions, which can have devastating consequences for the environment and the planet. Policymakers should prioritize this sector and implement measures to reduce emissions and prevent the potentially disastrous effects of carbon emissions on the atmosphere.

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doi.org

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