Lowcarbon transformation is a profound change in consumption patterns. Lowcarbon behavior choices are of great significance for reducing greenhouse gas (GHG) emissions and achieving carbon neutrality. This paper proposes a method for systematically estimating the GHG emissions of online and offline retail and catering consumption. Taking Beijing as an example, this paper conducts 160 000 Monte Carlo simulation experiments to account for GHG emissions under different consumption styles and then analyzes the emission reduction strategies of lowcarbon consumption. On average, the GHG emissions from online retail and food delivery are higher than those from traditional offline consumption. The high emissions of online consumption mainly come from the transportation and packaging of express delivery, while the emissions of offline consumption largely result from consumers’ trips during shopping and catering. The study develops a distanceemission curve, revealing the critical point that online and offline consumption choices should follow. The results show that the lowcarbon intensity of online or offline consumption is related to the distance between consumers and shops or restaurants, as well as the mode of transportation. If consumers walk or travel by public transportation, offline consumption is generally more lowcarbon. However, if the store or restaurant is far away and the consumer drives, online consumption will be more lowcarbon. Reducing the GHG emissions from packaging, transportation, delivery, travel, and consumption behavior is an effective reduction strategy. The results show that different strategy choices can reduce GHG emissions from goods or foods consumption by up to 94