Results for Individual Scenarios in the Wine Distribution Emissions Model: A Follow-Up

This post is a follow-up on Friday’s post examining the energy and carbon footprint of wine distribution in the United States.  In that post, I set the stage for why the authors’ chose to study this topic, and also presented 9 different scenarios by which 6 bottles of wine traveled from a winery in Sonoma to an individual consumer either locally in San Francisco, or long-distance in Manhattan.  I summarized the overall results of the study, but did not include the results from each individual scenario, due to space and time considerations.

This post is to present to you the individual results from that study.  For the introduction, methods, and general conclusions, I encourage you to skim through the original post by clicking here.

Results of each individual scenario

Local Scenarios

1)      3-tier distribution:

·         31Mj of energy were utilized, resulting in 2.18kg of CO2 emitted.

·         Emissions associated with the transportation of wine from the winery to the retail store (0.46kg CO2 per 6 bottles) were significantly greater than the emissions associated with the storage of the wine in the warehouse (0.04kg CO2 per 6 bottles) by a factor of 10.

·         The most energy-intensive link is the process is the last one: the consumer driving to the retail store account for ¾ of the total supply chain emissions.

oPer case energy usage is much lower for freight vehicles, which are frequently more highly utilized than personal vehicles.

·         If a consumer in San Francisco walks or takes highly utilized public transportation, 0.50kg CO2 per 6 bottles would be emitted, rather than 2.18kg CO2, which includes driving to the retail store.

·         If a consumer drives to the retail store, but stocks up on multiple purchases to greater utilize their vehicle, emissions equate to 0.90kg CO2 per 6 bottles, compared to 2.18kg CO2.

2)      3-tier distribution via retailer warehouse:

·         Including the extra step of staying in a retailers’ warehouse before traveling to the retail shop increases energy and emissions by 3%.

3)      Winery self-distribution:

·         Since many wineries may not be sending large orders to a retailer, the model compares the use of a light truck to a midsized truck (assuming 100% utilization), the choice of which has a significant impact on emissions.

·         If the winery can generate a large enough volume of sales to use a midsized truck, then this scenario is more efficient (1.89kg CO2 per 6 bottles) than the previous scenario that uses a distributor (2.18kg CO2 per 6 bottles).

·         If the winery does not generate a large enough volume of sales and requires use of a light truck, then the previous scenario of using a distributor with a midsized truck is more efficient then self-distributing using a light truck.

oA 100% utilized light truck results in about 5 times greater emissions (0.94kg CO2 per 6 bottles) than those of a 100% utilized midsized truck (0.19kg CO2 per 6 bottles).

4)      Fulfillment via third party logistics (3PL):

·         The direct shipping option produces the lowest emissions of all for the local scenarios: 0.42kg CO2 per 6 bottles, which is 19% of the emissions of the first 3-tier scenario.

oThe big improvement here is eliminating the need for the customer to drive to the retail store.

·         One caveat in this scenario is that if the winery had to drive the orders to a FedEx drop point (or some other via point), the emissions of this scenario would likely increase.

5)      Consumer picks up directly from the winery:

·         Driving an average car to the winery results in the greatest emissions produced (33.75kg CO2 per 6 bottles), equating to about 15 times greater emissions than the 3-tier scenario.

·         If the consumer drove a more efficient car, similar to a Toyota Prius, emissions for this scenario would be 14.5kg CO2 per 6 bottles, which is still over 6 times the emissions of the 3-tier scenario.

·         If the consumer picked up multiple orders (33 half cases) in a midsized pickup truck, emissions would drop to 1.43kg CO2 per 6 bottles, though this assumes that other individuals are not driving to this consumers house to pick up each of their individual orders.

oEven if this scenario occurred (which is unlikely), emissions per 6 bottles are still higher than those in the 3PL scenario, since large personal vehicles are not as efficient as well-utilized commercial vehicles.

Long-distance Scenarios

1)      3-tier distribution:

·         48.61Mj of energy were utilized and 3.62kg CO2 per 6 bottles was emitted.

o   These emissions are 66% more than those from the local 3-tier distribution scenario.

·         The California and New Jersey distribution centers contributed the most to the emissions; however, they were relatively efficient, accounting for about 78% of the emissions, but 96% of the distance traveled.

o   The Manhattan consumer traveled a shorter distance by car than the San Francisco consumer, and resulted in the least amount of emissions produced from all of the steps in the scenario.

·         Emissions from cold storage had minimal impact on the scenario.

2)      Fulfillment via 3PL ground delivery:

·         There were negligible differences between this scenario and the 3-tier scenario.

3)      Fulfillment via 3PL airfreight:

·         Using an air instead of ground delivery option, the emissions were increased by a factor of 7. 

oFlying 6 bottles of wine from San Francisco to Newark, NJ resulted in emissions of over 25kg CO2.

·         Air travel was responsible for 98% of the emissions for this scenario.

4)      Fulfillment via 3PL rail:

·         Total emissions for a rail travel option were 60% less than that of the 3PL with trucking option.

oEven though the route added over 800km to the trip by routing through Chicago, the lower energy use by the rail resulted in half of the emissions of the truck option.

Comparison across all scenarios

The following information through the conclusion was included in the original post, however, I’ll include it and the conclusions here as well, so as to avoid any back and forth between windows.

  •       When comparing all of the scenarios, significant differences in emissions are prevalent.
  •       The least efficient scenario was driving a car straight to the winery, which resulted in 80 times the emissions that would occur if the local delivery were handled via a 3PL scenario.

o   The least efficient scenario involved the one with the least distance traveled, indicating that the utilization of the vehicle is almost always more important than the physical distance traveled.

  •       The long distance 3PL viarail scenario had emissions equivalent to standard local 3-tier distribution scenario.

o   Total emissions for the 3PL rail scenario were about 60% of those associated with truck transport, and about 8% of those associated with air transport.


The authors of this study make several recommendations to wineries, based on the individual scenario results.  First, wineries should focus more on transportation of their wines as opposed to the storage and packaging of them, since the latter two do not significantly contribute to the overall carbon footprint of wine distribution.  Wineries should stockpile their wines when possible, that way they are greater able to utilize the vehicles by which their ways are transported. 

For long distance transport, the cross-country transit step contributes the most to emissions.  Wineries should try and utilize the most efficient transport type possible, which according to these results, is the railways or airlines.  For the local transport scenarios, the step from the retailer to the consumer generates the greatest levels of emissions.  If the consumer is able to walk or take public transportation, rather than drive to the store, this would be beneficial to reducing overall emissions.  Since this may not be feasible for many consumers, wineries may wish to promote the use of 3PL’s instead, supporting a more direct-to-consumer approach, and a reduction in emissions.

Since wineries most likely do not wish to discourage consumers from visiting their tasting rooms, they may encourage consumers/wine club members to pick up wines not only for themselves, but for other members as well, in order to avoid the addition emissions from another underutilized vehicle.  Volume discounts or discounts on shipping costs may also encourage consumers to greater utilize their vehicles, and reduce carbon emissions.  Sometimes just by simply mentioning that the winery is taking strides to lower their carbon emissions and educated consumers on the inefficiencies of the transport link in the system, then the consumers themselves may be motivated to try and do their part in reducing the carbon footprint as well.


This study, of course, is not without its’ faults.  It makes many assumptions that may or may not be applicable or true in real world situations.  Any changes to these assumptions, and the results would be altered.  For example, if the utilization rates were less than 100%, the overall emissions would get larger, however, the relative rankings of each scenario would likely not change, unless some vehicles utilization rates were uncharacteristically different from the rest for one reason or another.

It is also likely that these scenarios wouldn’t play over exactly the way they were set up in the model over and over again, and situations would be different depending on the particular day(s) the scenarios occurred.  Any number of variables could make the trek different from day to day, thus potentially altering the results.

One final problem is that every winery is different in regards to how it distributes their wines.  For example, some wineries out “in the middle of nowhere” will encounter much greater difficulty in using one scenario versus another, and perhaps the most efficient scenario in general is not the most efficient scenario for that individual winery.  Models need to be run on individual wineries in order to determine the proper scenario that is most energy efficient and economically feasible for that specific winery.

Please feel free to read the original post for more details on the background and methods of the study.  If, when reading these individual scenario results, your interpretations of the results have changed, please feel free to explain why below!
I am not a health professional, nor do I pretend to be. Please consult your doctor before altering your alcohol consumption habits. Do not consume alcohol if you are under the age of 21. Do not drink and drive. Enjoy responsibly!

2 comments for “Results for Individual Scenarios in the Wine Distribution Emissions Model: A Follow-Up

  1. M.Alan
    September 25, 2012 at 7:36 am

    This is very good research analysis, I read it but few assumption that have been taken into account is somewhat not practical in real world. Overall it is a good.

    • Becca
      September 25, 2012 at 7:55 am

      Thanks for your comment.

      All assumptions were based off real world scenarios, thus are by nature practical (since they exist already). Some may happen more frequently than others, but all of the scenarios studied were taken from real world situations.

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