A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING

Hasan Uzel; Feyyaz Alpsalaz; Emrah Aslan; Yıldırım Özüpak

doi:10.51477/mejs.1693320

A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING

Abstract

This study evaluates Linear Regression, Random Forest, XGBoost and CatBoost to forecast global CO₂ emissions from 2001 to 2021 using the Global Carbon Project dataset (accessed via Our World in Data). A leakage free pipeline standardizes preprocessing, prevents temporal spillover and applies a consistent train–test protocol. Performance is summarized with MSE, RMSE, MAE, MAPE and R² to enable fair, reproducible comparisons. Linear Regression delivers the strongest out of sample accuracy (R² = 0.94, RMSE = 3.81, MAPE = 12.9%), reflecting predominantly linear and autoregressive dynamics. Boosting models (XGBoost, CatBoost) follow closely (R² > 0.914), capturing nonlinear fluctuations, whereas Random Forest is comparatively weaker (R² = 0.879). Feature importance analysis highlights short-term lags (lag₁–lag₂) as dominant predictors, corroborated by autocorrelation, partial autocorrelation and Augmented Dickey–Fuller tests. Overall, the study provides a transparent global baseline and a standardized evaluation protocol that can be extended to country-granular analyses and policy experiments. By clarifying when simple statistical models suffice and when ensemble approaches add value, the results offer evidence-based, actionable guidance for researchers and policymakers seeking interpretable, scalable tools for emissions monitoring, planning and policy relevant scenario design.

Keywords

Ethical Statement

Our study does not cause any harm to the environment and does not involve the use of animal or human subjects. Therefore, it was not necessary to obtain an Ethics Committee Report.

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Details

Primary Language

English

Subjects

Air Pollution Modelling and Control

Journal Section

Research Article

Authors

Hasan Uzel ^*
0000-0002-8238-2588
Türkiye

Feyyaz Alpsalaz
0000-0002-7695-6426
Türkiye

Emrah Aslan
0000-0002-0181-3658
Türkiye

Yıldırım Özüpak
0000-0001-8461-8702
Türkiye

Publication Date

December 30, 2025

Submission Date

May 8, 2025

Acceptance Date

September 26, 2025

Published in Issue

Year 2025 Volume: 11 Number: 2

DOI

https://doi.org/10.51477/mejs.1693320

IZ

https://izlik.org/JA32KS37TL

Cite

RIS / Bibtex

APA

Uzel, H., Alpsalaz, F., Aslan, E., & Özüpak, Y. (2025). A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING. Middle East Journal of Science, 11(2), 247-262. https://doi.org/10.51477/mejs.1693320

AMA

1.Uzel H, Alpsalaz F, Aslan E, Özüpak Y. A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING. MEJS. 2025;11(2):247-262. doi:10.51477/mejs.1693320

Chicago

Uzel, Hasan, Feyyaz Alpsalaz, Emrah Aslan, and Yıldırım Özüpak. 2025. “A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING”. Middle East Journal of Science 11 (2): 247-62. https://doi.org/10.51477/mejs.1693320.

EndNote

Uzel H, Alpsalaz F, Aslan E, Özüpak Y (December 1, 2025) A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING. Middle East Journal of Science 11 2 247–262.

IEEE

[1]H. Uzel, F. Alpsalaz, E. Aslan, and Y. Özüpak, “A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING”, MEJS, vol. 11, no. 2, pp. 247–262, Dec. 2025, doi: 10.51477/mejs.1693320.

ISNAD

Uzel, Hasan - Alpsalaz, Feyyaz - Aslan, Emrah - Özüpak, Yıldırım. “A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING”. Middle East Journal of Science 11/2 (December 1, 2025): 247-262. https://doi.org/10.51477/mejs.1693320.

JAMA

1.Uzel H, Alpsalaz F, Aslan E, Özüpak Y. A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING. MEJS. 2025;11:247–262.

MLA

Uzel, Hasan, et al. “A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING”. Middle East Journal of Science, vol. 11, no. 2, Dec. 2025, pp. 247-62, doi:10.51477/mejs.1693320.

Vancouver

1.Hasan Uzel, Feyyaz Alpsalaz, Emrah Aslan, Yıldırım Özüpak. A COMPREHENSIVE BENCHMARK OF LINEAR AND ENSEMBLE MACHINE LEARNING MODELS FOR GLOBAL CO₂ EMISSION FORECASTING. MEJS. 2025 Dec. 1;11(2):247-62. doi:10.51477/mejs.1693320

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https://doi.org/10.18038/estubtda.1891746