Quantifying the Climate Impact of Food Systems: A Time-Series Analysis for Policy and Food Security

Background: This study aims to examine the temporal relationship between greenhouse gas (GHG) emissions from national food systems and changes in annual mean temperature. Methods: Using FAO Food Balance Sheets, the Food System Greenhouse Gas Emission Factor Database (FS-GHGEF-D), and Global Data Lab temperature data from 2010 to 2021, we constructed country- and food-group–level time-series datasets. Analytical methods included time-series visualization, autocorrelation and partial autocorrelation (ACF/PACF) analysis, ARIMA modelling, and time-series regression. Findings: Results demonstrated a significant co-movement between food system–related GHG emissions and global mean temperature, with regression analysis indicating that a 1,000 Mt increase in emissions corresponded to a 0.039°C rise (p < 0.05, R² = 0.37). ARIMA forecasts further suggest a structural upward trend in emissions, increasing by ~975 Mt annually. Interpretation: These findings highlight the causal link between food production–based GHG emissions and climate change, identify climate-sensitive food categories, and provide quantitative evidence to support national dGHG reduction targets and climate-responsive food security policies. Funding: This work was supported by a National Research Foundation of Korea grant funded by the Korean government (No. RS-2024-00340840).