Analyst Report: Study: ‘Cyber Monday’ 50 Times Greener Than ‘Black Friday’


This research was conducted to model the environmental impact of purchasing consumer goods either online or through a trip to a retail outlet for an in-store purchase. The environmental impact, as indicated by greenhouse gas (GHG) emissions, is weighed by the relative dollar amounts spent during the traditional ‘Black Friday’ and ‘Cyber Monday’ purchasing windows. In summary, we find that the impact of an in-store purchase represents an increase of more than 15 times that of an online purchase; the overall impact of Black Friday is more than 50 times that of Cyber Monday.


An Economic Input-Output Life Cycle Assessment (EIO-LCA) model approach was used to model the relative GHG emissions of both an online and in-store purchasing scenario. The EIO-LCA model incorporates an input-output economic model of the U.S. economy with publicly available environmental emissions data.* Only the emissions associated with the store infrastructure, transportation to the store and product shipping scenarios were modeled for this simplified LCA. The results were then scaled according to published average expenditures for the ‘Black Friday’ and ‘Cyber Monday’ purchasing windows. Several simplifying assumptions were made including:

  • The trip to the store is specifically dedicated for the product purchase (i.e. there are no other impulse or planned in-store, same-trip purchases such as a lunch at a fast-food outlet)
  • The difference in packaging between the two scenarios is excluded from this analysis.
  • The impact of e-commerce was excluded from this simplified study.

This approach is best applied to a large and well-established retail company with existing e-commerce site and support (e.g. Target, Kohls).

Model Results

Given several assumptions and simplifications, the in-store impact represents an increase of more than 15 times that of an online shopper, as shown below.

Figure 1: Simplified comparison of greenhouse gas emissions for in-store vs. online purchasing of retail goods


Source: MindClick SGM

The National Retail Federation, during the late November 2009 holiday season, conducted a survey that asked consumers about their anticipated spending over the Holiday weekend. The questions did not make a distinction between shopping on ‘Black Friday’ or ‘Cyber Monday’. On average, consumers expected to spend $343 on in-store purchases and $104 on online purchases over the post-Thanksgiving weekend.[2]

The results of the scenario modeling were then scaled by these dollar figures to show the relative impact of in-store and online purchases made during the post-Thanksgiving holiday shopping weekend.

Figure 2: Greenhouse gas emission for Black Friday vs. Cyber Monday

Source: MindClick SGM


  • The relative GHG impact of in-store shopping is more than 15 times that of online shopping, when taking into account the store infrastructure, transportation and product shipping required.
  • For the entire Thanksgiving Day weekend, the GHG emissions for “Black Friday” (in-store purchases) are more than 50 times the GHG emissions of “Cyber Monday” (online purchases), though the exact duration of both days is expanding to the entire Thanksgiving Day weekend. Emissions associated with the store infrastructure constitute a substantial portion of the GHG emissions and even outweigh the emissions of getting to the store. Even if the buyer walked to the retail location,  an in-store purchase would emit more GHG than ordering online using this simplified model.

Further studies could benefit from additional retail-specific data, more information about e-commerce infrastructure, and a more comprehensive study of consumer holiday buying habits.

* Carnegie Mellon University Green Design Institute. (2008) Economic Input-Output Life Cycle Assessment (EIO-LCA), US 1997 Industry Benchmark model, available from: http://www. Accessed December 3-5, 2009.

Table of Contents

  1. Summary
  2. Introduction
  3. Methodology
    1. Functional Unit Comparison
    2. Model for in-store purchase
    3. Model for online purchase
    4. Assumptions
    5. EIO-LCA Model
  4. Model Results
    1. Storefront presence results
    2. Transportation to store results
    3. Shipping to home results
    4. Comparison
  5. Discussion
  6. Conclusions
  7. About the Author

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