Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study
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| Format: | Preprint |
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2026
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| author | Oyinlola, Salim Ajayi, Joshua Ibekwe, Gozie |
| author_facet | Oyinlola, Salim Ajayi, Joshua Ibekwe, Gozie |
| contents | This study examines long-term CO$_2$ 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$^2$ 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 renewable-dominant energy system, whereas Nigeria continues on an upward emissions trajectory driven by fossil dependence. The United States and China maintain moderate declines but require accelerated mitigation to reach their respective net-zero commitments. Russia's emissions remain largely flat under current conditions. These findings highlight the strong influence of energy structures on national decarbonization prospects and underscore the need for targeted energy policy reforms to align with global climate objectives. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_01105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study Oyinlola, Salim Ajayi, Joshua Ibekwe, Gozie Systems and Control This study examines long-term CO$_2$ 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$^2$ 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 renewable-dominant energy system, whereas Nigeria continues on an upward emissions trajectory driven by fossil dependence. The United States and China maintain moderate declines but require accelerated mitigation to reach their respective net-zero commitments. Russia's emissions remain largely flat under current conditions. These findings highlight the strong influence of energy structures on national decarbonization prospects and underscore the need for targeted energy policy reforms to align with global climate objectives. |
| title | Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.01105 |