Electricity consumption of anesthesia workstations and potential emission savings by avoiding standby.

Abstract

Background: Anesthesiology has a relevant carbon footprint, mainly due to volatile anesthetics (scope 1 emissions). Additionally, energy used in the operating theater (scope 2 emissions) contributes to anesthesia-related greenhouse gas (GHG) emissions.

Objectives: Optimizing the electricity use of medical devices might reduce both GHG emissions and costs might hold potential to reduce anaesthesia-related GHG-emissions and costs. We analyzed the electricity consumption of six different anesthesia workstations, calculated their GHG emissions and electricity costs and investigated the potential to reduce emissions and cost by using the devices in a more efficient way.

Methods: Power consumption (active power in watt , W) was measured with the devices off, in standby mode, or fully on with the measuring instrument SecuLife ST. Devices studied were: Dräger Primus, Löwenstein Medical LeonPlus, Getinge Flow C, Getinge Flow E, GE Carestation 750 and GE Aisys. Calculations of GHG emissions were made with different emission factors, ranging from very low (0.09 kg CO₂-equivalent/kWh) to very high (0.660 kg CO₂-equivalent/kWh). Calculations of electricity cost were made assuming a price of 0.25 € per kWh.

Results: Power consumption during operation varied from 58 W (GE CareStation 750) to 136 W (Dräger Primus). In standby, the devices consumed between 88% and 93% of the electricity needed during use. The annual electricity consumption to run 96 devices in a large clinical department ranges between 45 and 105 Megawatt-hours (MWh) when the devices are left in standby during off hours. If 80% of the devices are switched off during off hours, between 20 and 46 MWh can be saved per year in a single institution. At the average emission factor of our hospital, this electricity saving corresponds to a reduction of GHG emissions between 8.5 and 19.8 tons CO₂-equivalent. At the assumed prices, a cost reduction between 5000 € and 11,600 € could be achieved by this intervention.

Conclusion: The power consumption varies considerably between the different types of anesthesia workstations. All devices exhibit a high electricity consumption in standby mode. Avoiding standby mode during off hours can save energy and thus GHG emissions and cost. The reductions in GHG emissions and electricity cost that can be achieved with this intervention in a large anesthesiology department are modest. Compared with GHG emissions generated by volatile anesthetics, particularly desflurane, optimization of electricity consumption of anesthesia workstations holds a much smaller potential to reduce the carbon footprint of anesthesia; however, as switching off anesthesia workstations overnight is relatively effortless, this behavioral change should be encouraged from both an ecological and economical point of view.