This study delves an inventory system for decaying items that incorporates price-sensitive demand under learning effect and carbon emissions consideration. As Carbon emissions are a major cause of climate change, in recent times, to control carbon pollution and reduce emissions, several nations have levied carbon taxes on companies. During the process of moving goods from one location to another, carbon is released thus to reduce carbon emissions, we incorporated carbon emission costs owing to the transportation of ordered finished goods. In realism, carbon emissions are also generated throughout the deterioration process. Since almost every product deteriorates with time, the deterioration’s rate is thought to be time-reliant and follows weibull distribution (three-parameter). To make the study realistic, preservation technology is integrated for degradation goods. Shortages are expected to be backlogged partially during stock out phase. This study aims to minimize the system’s overall average cost by figuring out the optimum ordering quantity as well as time interval. For justification of the study, a numerical illustration is presented and to check the stability of the system a comprehensive sensitivity analysis is conducted to investigate how various parameters affect the optimal solution.