Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study
ネットゼロ移行のための時系列CO2排出予測とエネルギーミックス分析：多国間研究 (AI 翻訳)
Salim Oyinlola, J. Ajayi, Gozie Ibekwe
🤖 gxceed AI 要約
日本語
本研究は、ナイジェリア、米国、中国、ブラジル、ロシアの5カ国における長期CO2排出軌跡を、エネルギーミックス特性と時系列予測モデルを統合して分析。Holt-Winters法が多くの国で最も正確な予測を示し、ブラジルは低排出未来に最も近い一方、ナイジェリアは化石燃料依存で排出増加傾向にある。
English
This study analyzes long-term CO2 emission trajectories across five major economies (Nigeria, US, China, Brazil, Russia) using time-series forecasting models integrated with energy mix characteristics. Holt-Winters method outperformed others for most countries; Brazil aligns with low-emission future, while Nigeria shows upward trend due to fossil dependence.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本にとって直接的なケーススタディではないが、エネルギーミックス構造が排出経路に与える影響を定量的に示しており、日本のエネルギー政策やGX戦略の評価にも応用可能な分析枠組みを提供する。
In the global GX context
This multi-country comparison highlights the critical role of energy mix in shaping decarbonization pathways, offering a quantitative benchmark for global net-zero commitments and informing policy debates on energy transition.
👥 読者別の含意
🔬研究者:Provides a comparative forecasting methodology applicable to other countries or regions.
🏢実務担当者:Useful for energy planners and corporate strategists to assess long-term emission pathways under different energy scenarios.
🏛政策担当者:Insights on the divergence of national decarbonization trajectories, supporting targeted policy design.
📄 Abstract（原文）
This study examines long-term CO₂ 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 to 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 R² 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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.30574/gjeta.2026.26.1.0002first seen 2026-05-15 17:43:35
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