Carbon Footprint of Alternative Grocery Shopping and Transportation Options from Retail Distribution Centers to Customer.

The COVID-19 pandemic has accelerated the growth of e-commerce and automated warehouses, vehicles, and robots and has created new options for grocery supply chains. We report and compare the greenhouse gas (GHG) emissions for a 36-item grocery basket transported along 72 unique paths from a centralized warehouse to the customer, including impacts of micro-fulfillment centers, refrigeration, vehicle automation, and last-mile transportation. Our base case is in-store shopping with last-mile transportation using an internal combustion engine (ICE) SUV (6.0 kg CO2e). The results indicate that emissions reductions could be achieved by e-commerce with micro-fulfillment centers (16-54%), customer vehicle electrification (18-42%), or grocery delivery (22-65%) compared to the base case. In-store shopping with an ICE pick-up truck has the highest emissions of all paths investigated (6.9 kg CO2e) while delivery using a sidewalk automated robot has the least (1.0 kg CO2e). Shopping frequency is an important factor for households to consider, e.g. halving shopping frequency can reduce GHG emissions by 44%. Trip chaining also offers an opportunity to reduce emissions with approximately 50% savings compared to the base case. Opportunities for grocers and households to reduce grocery supply chain carbon footprints are identified and discussed.

Carbon implications of marginal oils from market-derived demand shocks.

Expanded use of novel oil extraction technologies has increased the variability of petroleum resources and diversified the carbon footprint of the global oil supply1. Past life-cycle assessment (LCA) studies overlooked upstream emission heterogeneity by assuming that a decline in oil demand will displace average crude oil2. We explore the life-cycle greenhouse gas emissions impacts of marginal crude sources, identifying the upstream carbon intensity (CI) of the producers most sensitive to an oil demand decline (for example, due to a shift to alternative vehicles). We link econometric models of production profitability of 1,933 oilfields (~90% of the 2015 world supply) with their production CI. Then, we examine their response to a decline in demand under three oil market structures. According to our estimates, small demand shocks have different upstream CI implications than large shocks. Irrespective of the market structure, small shocks (-2.5% demand) displace mostly heavy crudes with ~25-54% higher CI than that of the global average. However, this imbalance diminishes as the shocks become bigger and if producers with market power coordinate their response to a demand decline. The carbon emissions benefits of reduction in oil demand are systematically dependent on the magnitude of demand drop and the global oil market structure.

Life Cycle Greenhouse Gas Emissions for Last-Mile Parcel Delivery by Automated Vehicles and Robots.

Increased E-commerce and demand for contactless delivery during the COVID-19 pandemic have fueled interest in robotic package delivery. We evaluate life cycle greenhouse gas (GHG) emissions for automated suburban ground delivery systems consisting of a vehicle (last-mile) and a robot (final-50-feet). Small and large cargo vans (125 and 350 cubic feet; V125 and V350) with an internal combustion engine (ICEV) and battery electric (BEV) powertrains were assessed for three delivery scenarios: (i) conventional, human-driven vehicle with human delivery; (ii) partially automated, human-driven vehicle with robot delivery; and (iii) fully automated, connected automated vehicle (CAV) with robot delivery. The robot's contribution to life cycle GHG emissions is small (2-6%). Compared to the conventional scenario, full automation results in similar GHG emissions for the V350-ICEV but 10% higher for the V125-BEV. Conventional delivery with a V125-BEV provides the lowest GHG emissions, 167 g CO2e/package, while partially automated delivery with a V350-ICEV generates the most at 486 g CO2e/package. Fuel economy and delivery density are key parameters, and electrification of the vehicle and carbon intensity of the electricity have a large impact. CAV power requirements and efficiency benefits largely offset each other, and automation has a moderate impact on life cycle GHG emissions.