Cross-Country CO₂ Emission Forecasting Using Statistical and Neural-Network-Based Approaches
Year: 2026
Type: article
Abstract: Accurate forecasting of CO₂ emissions is essential for climate policy development, sustainable energy planning, and long-term environmental management. However, many existing studies are limited by single-country analyses, short temporal coverage, or reliance on a single forecasting methodology. To address these limitations, this study presents a comparative forecasting framework based on statistical and neural-network-based approaches for estimating annual CO₂ emission trends in six major emitting economies: China, the United States (USA), the European Union (EU27), India, Russia, and Japan. Using annual CO₂ emissions data obtained from the EDGAR database for the period 1970–2021, several statistical models (AUTO.ARIMA, HOLT, and TBATS) and neural-network-based approaches (NNETAR, MLP, and ELM) were comparatively evaluated. Forecasting performance was assessed using Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). The results demonstrate that model performance varies significantly depending on the emission dynamics of each country. Neural-network-based approaches exhibited strong predictive capability for rapidly changing and nonlinear emission trends, particularly in China and Russia, whereas statistical models such as TBATS and AUTO.ARIMA provided competitive forecasting accuracy for more stable emission patterns observed in the USA and Japan. Among all evaluated approaches, TBATS achieved the highest forecasting performance for India and the USA, while MLP and NNETAR models produced robust results for China and EU27. This study contributes to the literature by providing a long-term cross-country comparative evaluation of statistical and neural-network-based forecasting approaches for CO₂ emissions. The findings offer an evidence-based analytical framework that can support policymakers in energy transition planning, emission reduction strategies, and sustainable development policies aligned with global climate objectives.
Author: Zeydin Pala
Institution: Muş Alparslan University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W7166645142