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Objectives Overuse—including overdiagnosis and unnecessary testing—can result in avoidable environmental harms. In the case of cancer, frequent imaging tests contribute to higher healthcare costs and increased carbon emissions without clear patient benefit. While methods to include these carbon emissions in health economic evaluations are emerging, real-world case studies are limited. Regular surveillance imaging following curative treatment for stage III melanoma is one example of this kind of testing. These patients undergo frequent CT, PET, PET/CT, and other imaging tests with the goal of detecting recurrences early. However, the survival benefit of more frequent testing has not been demonstrated. The aims of this study are to (i) estimate the carbon emissions associated with different frequency schedules for imaging surveillance after curative treatment for stage III melanoma, and to (ii) compare methods to account for these environmental impacts in a health economic evaluation.Method An existing decision analytic health economic model and cost-effectiveness analysis was identified that compared different imaging follow up schedules for stage III melanoma patients: 3–4-monthly, 6-monthly, 12-monthly and no routine imaging. The model estimates the cost per accurate diagnosis of distant melanoma from a health system perspective and does not include carbon emissions. We will update the existing model to include the carbon emissions associated with imaging, clinic appointments, confirmation tests, and treatment. The model will be re-run following various methods to demonstrate the impact of carbon emissions on the analysis. The methods demonstrated will include: (1) reporting emissions parallel to the incremental cost effectiveness ratio (ICER), (2) reporting the incremental carbon footprint effectiveness ratio (ICFER) and incremental carbon footprint cost ratio (ICFCR) parallel to the ICER, and (3) integrating emissions into the ICER as a monetary cost.Results Carbon emissions were higher with more frequent imaging schedules. Emissions were highest in the 3- and 6-monthly groups (230 kg per patient-year), followed by the 12-monthly imaging group (149 kg CO2-e per patient-year), and the no routine imaging group (49 kg CO2-e per patient-year). When valued in dollars using New South Wales Government Carbon Values these impacts accounted for $6 to $30 per patient-year. These results will be used as inputs in the model to demonstrate available methods. Full results comparing methods to include these impacts in the economic evaluation will be presented at the conference.Conclusions More frequent surveillance imaging of stage III melanoma patients is associated with higher healthcare costs and environmental impacts. It is feasible to include carbon emissions in economic evaluations and there are various methods available to do so.