With growing regulatory emphasis on reducing carbon emissions, the food industry increasingly requires inventory strategies that align economic goals with environmental sustainability. This study develops an inventory model for food products experiencing non-instantaneous deterioration, integrating investment in advanced preservation technologies that influence the degradation rate, shelf life, and dynamic demand sensitive to pricing and preservation quality under two cases (Case 1: \({t}_{d}<{t}_{1}\) and Case 2: \({t}_{1}<{t}_{d}\) ) based on the time interval of non-deterioration time \(({t}_{d})\) and ending inventory time point \(\left({t}_{1}\right)\) . The model further incorporates trade credit options that account for default risk and customer backlog influenced by waiting time. Two carbon control mechanisms: cap-and-trade (CAT) and carbon tax (CT) are assessed within the model. A mathematical optimization framework is applied to enhance average revenue while meeting emission regulations. To obtain the optimal solutions, analytical derivations are presented, and an algorithm is developed to attain the global optimal solution. Comparative analysis indicates that the CAT system is more effective than CT in terms of both profit growth ($596.02 and $560 more for case 1 and 2 respectively) and emission reduction (75.84 kgCO2 and 28 kgCO2 less for case 1, 2 respectively). Sensitivity analysis on major parameters demonstrates their impact on inventory decisions and environmental performance. These insights can guide decision-makers in the food sector toward more efficient and eco-compliant inventory policies, with cap-and-trade emerging as the preferred regulatory approach.
