Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

Researchers propose a federated learning framework that combines ARIMA, GARCH, LSTM-Attention, and XGBoost models to forecast global carbon emissions while preserving data privacy. The system enables collaborative forecasting across distributed clients without sharing raw data, achieving R² values averaging 0.73 across 14 test clients.

This research addresses a critical intersection of climate science and data governance. As nations commit to carbon neutrality targets, accurate emissions forecasting becomes essential for policy-making. However, emissions data remains fragmented across countries and industrial sectors, with collection restricted by privacy regulations and geopolitical sensitivities. The federated learning approach solves this coordination problem by allowing organizations to train predictive models collaboratively without exposing sensitive data.

The technical innovation lies in the hybrid architecture combining statistical models (ARIMA for trends, GARCH for volatility) with deep learning (LSTM-Attention) and gradient boosting (XGBoost). This heterogeneous approach leverages the strengths of each methodology while mitigating individual weaknesses. The experimental results demonstrate practical viability, with average MAPE of 6.5% and R² of 0.73—adequate precision for strategic policy guidance.

For the broader sustainability and AI infrastructure sectors, this work demonstrates how privacy-preserving machine learning can unlock value from previously inaccessible datasets. Organizations managing carbon credit markets, ESG reporting platforms, and climate-focused fintech solutions could deploy similar federated architectures. The regulatory-compliant nature addresses growing data protection requirements, making international climate collaboration more feasible.

The framework's scalability and modular design suggest potential applications beyond emissions forecasting—energy grids, supply-chain resilience, and cross-border epidemiological tracking could benefit from comparable approaches. However, real-world deployment will depend on adoption by government agencies and major industrial emitters, which requires institutional coordination beyond technical innovation.

• →Federated learning enables accurate carbon emissions forecasting without sharing raw data across countries and sectors.
• →The hybrid model combining ARIMA, GARCH, LSTM-Attention, and XGBoost achieves 6.5% average MAPE across diverse clients.
• →Privacy-preserving architecture addresses regulatory constraints that typically fragment emissions data collection globally.
• →Framework demonstrates potential scalability beyond climate science to energy, epidemiology, and supply-chain forecasting.
• →Practical viability depends on institutional adoption by governments and industrial emitters rather than technical capability.