Abstract
This study assessed the fuel requirement and annual fuel-based heating cost of conventional diesel (F0), raw biomass (F1), low-severity torrefied biomass (F2), and high-severity torrefied biomass (F3) for greenhouse heating across different climatic regions of Korea. Heating demand was estimated for seven major horticultural regions using climate-based data and crop-specific day/night setpoint temperatures and cultivation periods for strawberry, tomato, and paprika, which represent greenhouse crops with contrasting thermal requirements and production schedules. Fuel consumption and annual fuel-based heating cost were then compared across fuel options. Additional analyses included price sensitivity testing, an equal-efficiency scenario, transport-distance scenarios, break-even analysis, and decision-boundary mapping. Annual heating demand was lowest in Jeju and highest in Chuncheon for all crops, and increased in the order of strawberry, tomato, and paprika. Under the baseline assumptions, high-severity torrefied biomass (F3) showed the lowest estimated fuel requirement and annual fuel-based heating cost across all crops and regions. This ranking was maintained under the equal-efficiency scenario and under transport-distance scenarios from 0 to 200 km, in which both the fixed biomass logistics cost and the distance-dependent transport cost were included. In the decision-space analysis, F3 occupied the largest share (53.49%), followed by F1 (39.34%) and F2 (7.17%), indicating that F3 was not universally dominant across all price conditions. The break-even prices relative to raw biomass were 0.294 USD/kg for F2 and 0.442 USD/kg for F3 under the baseline transport condition. Overall, under the assumed fuel properties, delivered-price structure, transport-cost assumptions, and standardised greenhouse conditions, high-severity torrefied biomass showed favourable fuel-cost performance among the evaluated scenarios. However, its competitiveness was conditional on the interaction between raw biomass price, torrefaction premium, transport distance, and greenhouse heat-demand conditions. Therefore, the results should be interpreted as a screening-level fuel-cost comparison rather than as a direct prediction of farm-level heating bills.