Modeling Global Warming from Agricultural CO2 Emissions: From Worldwide Patterns to the Case of Iran

Agriculture is a major source of greenhouse gas emissions, yet predicting temperature increases associated with specific CO2 sources remains challenging due to the heterogeneity of agri-environmental systems. In response, this study presents a machine learning framework that adopts an agri-food system boundary (production to retail) and combines systematic model benchmarking, interpretability, and a multi-scale perspective. Seven regression models, including tree ensembles and deep learning architectures, are evaluated on a harmonized dataset covering 236 countries over the 1990–2020 period to forecast annual temperature increases. Results show that gradient-boosted decision trees consistently outperform deep learning models in predictive accuracy and offer more stable feature attributions. Interpretability analysis reveals that spatio-temporal variables are the dominant drivers of global temperature variation, while environmental and sector-specific factors play more localized roles. A country-level case study on Iran illustrates how the framework captures national deviations from global patterns, highlighting intensive rice cultivation and on-farm energy use as key influential factors. By integrating high-performance predictions with interpretable insights, the proposed framework supports the design of both global and country-specific climate mitigation strategies.