目的 调查北京、长沙、上海的5家血液净化中心在透析机电力消耗与患者通勤方面的碳足迹现状，为制定血液透析治疗碳足迹量化方法提供科学依据。 方法 采用横断面调查方法，于2024年1月─12月收集5家血液净化中心相关数据。使用电量计量插座实测各型号透析机单次治疗耗电量，并通过匿名问卷获取患者通勤距离与交通方式。基于分省份电力与交通方式排放因子计算碳足迹。 结果 单次血液透析在透析机电力消耗与患者通勤方面的碳足迹分别为(1.67±0.38)kg与(1.53±0.56)kg二氧化碳当量(carbon dioxide equivalents，CO2-eq)。患者通勤碳足迹的变异系数为37%。相同型号透析机在北方中心的单次耗电量和碳足迹比南方中心高出25%和30%以上。通勤距离亚组分析显示，各组间(≤4 km、＞4～8 km、＞8～16 km、＞16 km)交通碳足迹差异具有统计学意义(H=320.662，P＜0.001)。中位数从≤4 km组的0.00(0.00,0.06)kg CO2-eq增至＞16 km组的7.55(0.37,9.74)kg CO2-eq。各组交通方式构成亦存在显著差异(χ²=274.661,P＜0.001)，表现为随距离增加由步行、自行车为主(62.3%)转向汽车为主(55.6%)。结论 我国通勤碳足迹低于国外报道，可能与我国公共交通发达有关。建议在患者管理中纳入通勤距离考量，推动转诊至社区中心，从源头降低碳足迹。电力消耗存在显著地域差异，提示可通过优化设备运行环境实现减排。

Objective To investigate current carbon footprint status of five hemodialysis centers regarding energy consumption of dialysis machine and patient commuting, so as to provide a scientific basis for quantification of carbon footprint in hemodialysis treatment.  Methods A cross-sectional survey approach was employed. Data collection was conducted from January to December 2024 in five hemodialysis centers in Beijing, Changsha, and Shanghai. Electricity consumption per hemodialysis session and per dialysis machine was measured by a plug-in energy meter. Resident distance and traffic mode of the patient were obtained via anonymous questionnaires. Emission factors for provincial electricity and transportation mode were used to calculate the carbon footprint.  Results  Carbon footprint of a hemodialysis session attributable to electricity consumption by a hemodialysis machine and patient’s commuting were 1.67±0.38 kg and 1.53±0.56 kg of carbon dioxide equivalents (CO2-eq), respectively. The coefficient of variation for carbon footprint of patients’ commuting was 37%. For the same type of dialysis machines, electricity consumption and carbon footprint for a hemodialysis session in northern centers were 25% and 30% higher than those in southern centers. Commuting distance analysis revealed that carbon footprint for commuting was statistically different among the groups of different resident distance (≤4 km, ＞4～8 km, ＞8～16 km, and＞16 km; H=320.662, P＜0.001), with the medium value of 0.00 (0.00, 0.06) kg CO2-eq in ≤4 km group to 7.55 (0.37, 9.74) kg CO2-eq in＞16 km group; commuting mode was also statistically different among groups (χ²=274.661, P＜0.001), with traffic mode from walking or bicycle (62.3%) to car or bus  (55.6%) along with the increase of resident distance.  Conclusion Carbon footprint for commuting of hemodialysis patients is lower in China than in foreign countries, probably due to the advanced system of public transportation in China. We recommend that commuting distance should also be considered in the management of hemodialysis patients, encouraging patients to use hemodialysis facilities in nearby community centers to reduce carbon consumption. Moreover, optimization of the operation environment for dialysis machines may reduce the electricity consumption due to geographic differences.