The carbon footprint of China's import coffee: a scenario analysis

Abstract

China’s rapidly expanding coffee market has triggered a structural shift in global trade, prioritizing long-haul imports of Arabica beans over regional sourcing, an operational reality that has caused maritime emissions to rise significantly faster than trade volumes. This study aims to quantify the historical maritime CO₂ emissions of this specific supply chain and project its future carbon footprint to determine the most effective levers for decarbonization. To achieve this, a bottom-up, port-to-port stochastic simulation model was constructed in Python, utilizing the Energy Efficiency Operational Indicator (EEOI) to analyze trade flows from the top five origin countries to China and integrating decelerating demand forecasts with 20 distinct scenarios to test the system's sensitivity to random disruptions, strategic sourcing shifts, and five specific decarbonization pathways. Historical analysis (2015-2024) confirmed that sourcing distance was the primary driver of emissions, which grew 618% against a 352% volume increase; however, the forecast (2025-2050) reveals that technological efficiency is the only dominant lever for the future, with the "High Adoption" technology profile achieving a 53% absolute decrease in annual emissions by 2050 compared to the 2024 baseline, whereas the "Gradual Nearshoring" scenario failed to reverse the absolute growth trend. This research provides a granular, route-specific assessment of the carbon intensity of China's green coffee imports, challenging the efficacy of sourcing-based solutions in terroir-driven industries by demonstrating that supply chain restructuring is less effective than operational efficiency improvements. While limited to the port-to-port maritime segment and relying on interpolated fleet-wide efficiency data, the findings imply that industry decarbonization efforts should prioritize the acceleration of fleet modernization and drop-in fuels, such as sustainable biofuels, over unrealistic attempts to alter established trade routes.

China’s rapidly expanding coffee market has triggered a structural shift in global trade, prioritizing long-haul imports of Arabica beans over regional sourcing, an operational reality that has caused maritime emissions to rise significantly faster than trade volumes. This study aims to quantify the historical maritime CO₂ emissions of this specific supply chain and project its future carbon footprint to determine the most effective levers for decarbonization. To achieve this, a bottom-up, port-to-port stochastic simulation model was constructed in Python, utilizing the Energy Efficiency Operational Indicator (EEOI) to analyze trade flows from the top five origin countries to China and integrating decelerating demand forecasts with 20 distinct scenarios to test the system's sensitivity to random disruptions, strategic sourcing shifts, and five specific decarbonization pathways. Historical analysis (2015-2024) confirmed that sourcing distance was the primary driver of emissions, which grew 618% against a 352% volume increase; however, the forecast (2025-2050) reveals that technological efficiency is the only dominant lever for the future, with the "High Adoption" technology profile achieving a 53% absolute decrease in annual emissions by 2050 compared to the 2024 baseline, whereas the "Gradual Nearshoring" scenario failed to reverse the absolute growth trend. This research provides a granular, route-specific assessment of the carbon intensity of China's green coffee imports, challenging the efficacy of sourcing-based solutions in terroir-driven industries by demonstrating that supply chain restructuring is less effective than operational efficiency improvements. While limited to the port-to-port maritime segment and relying on interpolated fleet-wide efficiency data, the findings imply that industry decarbonization efforts should prioritize the acceleration of fleet modernization and drop-in fuels, such as sustainable biofuels, over unrealistic attempts to alter established trade routes.