Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study
Salim Oyinlola, Joshua Ajayi, Gozie Ibekwe

TL;DR
This paper analyzes long-term CO2 emission trajectories in five major economies using time-series models, revealing divergent decarbonization pathways influenced by energy structures and policy needs for global climate goals.
Contribution
It integrates energy-mix data with multiple forecasting models to evaluate and compare long-term emissions trajectories across diverse national contexts.
Findings
Holt-Winters model most accurate for Nigeria, US, China, Brazil
SARIMA best for Russia due to stable emissions
Brazil's renewable energy favors low-emission future
Abstract
This study examines long-term CO emission trajectories across five major economies: Nigeria, the United States, China, Brazil, and Russia, by integrating national energy-mix characteristics with time-series forecasting models. Annual emissions from 2000-2023 were analyzed alongside energy production data to classify countries into fossil-dependent, transition-phase, or renewable-accelerated profiles. Three forecasting models (ARIMA, SARIMA, and Holt-Winters exponential smoothing) were evaluated using MAE, RMSE, MAPE, and R metrics. Results show that Holt-Winters provided the most accurate forecasts for Nigeria, the United States, China, and Brazil, while SARIMA performed best for Russia due to its relatively stable emissions. Long-term projections from 2024 to 2060 indicate divergent decarbonization pathways. Brazil aligns most closely with a low-emission future owing to its…
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Taxonomy
TopicsEnergy, Environment, and Transportation Policies · Energy, Environment, Economic Growth · Climate Change Policy and Economics
