1.Explain what the t-test results in Figures C12.4 and C12.5 mean, stating an appropriate hypothesis and null hypothesis for each.2. With regard to Rebecca’s research question, what would you conclude from these results about the importance of distance travelled as opposed to type of vehicle used?3. Comment on Rebecca’s analysis and the assumptions she has made. What are the implications of this for the validity and reliability of her findings?4. What data would Rebecca need and what sort of analysis could Rebecca do in order to take her research further as her project tutor suggests, such as seeing how different aspects of the supply chain affect total carbon emissions for different products within that supply chain?
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Case 12e: Food Miles, Carbon Footprints and Supply Chains
David Oglethorpe, Northumbria University
During her Business degree, Rebecca had become interested in the
environmental impacts of business activity and, in particular, the concept that
lean supply chains use less resources per unit of output and that the leaner
systems tended to be larger scale. The modules she had taken, particularly in
sustainable business management, introduced several case studies and
literature which appeared to have conflicting messages, in particular surrounding
the ‘food miles’ debate. Some literature suggested smaller-scale, localised
systems may be better for the environment and others advocated larger-scale
systems. She decided to try to find some answers to this by collecting data for
her research project, which she titled:
‘How do small- and large-scale distribution systems differ in terms of distance
travelled and their carbon footprint?’
Rebecca’s project tutor felt this was a good question to ask but suggested she
might want to take it further by examining within whichever system seemed to
have the lowest carbon footprint, how different aspects of that supply chain affect
total carbon emissions for different products (Raw Materials, Production,
Processing, Manufacture, Retail, and Consumption). Her tutor also
recommended some useful secondary data and literature to help work out the
carbon footprint of different distribution options.
During her literature search Rebecca found three papers that were particularly
useful as they outlined the type of data she needed to collect (Bimpeh et al.,
2006; Coley et al., 2009; Oglethorpe, 2010). To discover whether small- and
large-scale distribution systems differed in terms of distance travelled and their
carbon footprint, she needed data about the distance travelled using different
distribution systems and how much carbon was emitted for each kilometre
travelled, by different vehicles. Rebecca then needed to compare small-scale
distribution systems using small-scale vehicles with large-scale distribution
systems using large-scale vehicles. To try and make any differences between
these systems obvious, she decided to contrast farm shops with supermarkets.
Collecting data for farm shops
Rebecca contacted regional food groups and obtained names and addresses of
100 farm shops. She emailed each of them asking if she could provide a very
brief questionnaire for their shoppers to complete. This questionnaire asked
shoppers two simple questions and was to be distributed on paper at the
checkout, customers placing their completed questionnaire in a box. The
questionnaire was as follows:
Farm Shop Questionnaire
My name is Rebecca Smith and I am a student at the University of Anytown. For my
research project I am studying the distance people travel to do shopping at different
types of shops. The farm shop has kindly let me ask you two very simple questions
about your visit today and I would be very grateful if you would be able to fill your
answers in below and post your completed questionnaire in the box provided. Your help
is very much appreciated.
1) How far have you travelled to come to the farm shop today?
_________ miles, or ________ kilometres
2) What type of transport did you use to get to the farm shop today?
Thank you very much again for your help.
Student, University of Anytown
Eventually, Rebecca was able to obtain responses to her questionnaire from
shoppers at 35 farm shops. From these she calculated the mean (average)
distance that shoppers travelled, all shoppers having travelled by car, which
Rebecca had to assume was an ‘average car’ as defined by Defra (2010).
Rebecca remembered to convert all ‘miles’ to kilometres’.
Once she had gathered this data, Rebecca contacted each of these 35 farm
shops and having thanked them for their generous help so far, asked how far
their produce travelled from the Distribution Centre they used and what sort of
vehicle was used. 28 farm shops were able to provide this information, all using
a light freight vehicle, rigid body, weighing between 3.5 to 7.5 tonnes (Figure
Figure C12.1 A typical light freight vehicle, rigid body, weighing between
3.5 to 7.5 tonnes
Rebecca now had complete data for 28 farm shops detailing the mean distance
that shoppers travelled, the type of transport shoppers used and the distance
produce travelled from the Distribution Centre to the shop.
Collecting data for supermarkets
Rebecca now needed to collect equivalent data for supermarkets. One particular
supermarket group had a loyalty card scheme. Through this the supermarket
collected data on the produce bought, as well storing where the shopper lived
and how they travelled to the supermarket.
Rebecca contacted this supermarket group and asked if they could provide the
average distance customers travelled to a sample of 28 different stores and for
each store, how far produce travelled from their Distribution Centre to that store.
The supermarket chain provided Rebecca with the data she needed but she had
to assume all journeys to the supermarkets were made using an ‘average car’.
They also told her that all produce was transported using 33 tonne articulated
heavy goods vehicles (Figure C12.2).
Figure C12.2 A typical 33 tonne articulated heavy goods vehicle
Rebecca typed the data for the 28 farm shops and 28 supermarkets into her
spreadsheet software in preparation for her analysis (Figure C12.3).
Data for the mean customer and distribution journey
distances at 28 farm shops and 28 supermarkets
Rebecca decided to use an independent sample t-test (assuming unequal
variances) to see if there was a significant difference between the total mean
distances (kilometres) travelled by food purchased from the farm shops and
supermarkets. This was referred to as a ‘two sample t-test’, its alternative name,
in her spreadsheet software. Using the spreadsheet, Rebecca obtained the
output in Figure C12.4.
Spreadsheet output for an independent sample t-test for
the difference between the mean total journeys
To work out the carbon footprint of each farm shop’s or supermarket’s mean
distance (km) travelled by food purchased, Rebecca needed to know how much
carbon dioxide was emitted per tonne of produce for each type of vehicle for
every kilometre travelled. Fortunately this data is published by the UK’s
Department of Environment, Food and Rural Affairs (Defra) assuming average
loads are carried (Table C12.1).
Table C12.1 Carbon dioxide-equivalent (CO2-e) emissions from different
vehicles used in the journeys
Average Car (unknown fuel)1
Rigid Truck, 3.5t-7.5t (diesel)2
Articulated HGV, 33t (diesel)2
0.24579 kg CO2-e per km travelled
0.79456 kg CO2-e per tonne-km
0.10462 kg CO2-e per tonne-km
Source: Developed from Defra (2010) 1Table 6e; 2Table 7e
By multiplying each total mean distance in Figure C12.3 by the corresponding
carbon dioxide-equivalent emissions in Table C12.1, Rebecca transformed all the
mean distance (km) travelled by food purchased data into a corresponding
carbon footprint for each of the 28 farm shops and supermarkets. Again,
Rebecca decided to use an independent sample t-test (assuming unequal
variances) to test if there was a significant difference between the total carbon
emissions for each of the two systems.
Using her spreadsheet as before, Rebecca obtained the output in Figure C12.5.
Spreadsheet output for independent sample t-test for
the difference between the mean carbon emissions
1. Explain what the t-test results in Figures C12.4 and C12.5 mean, stating
an appropriate hypothesis and null hypothesis for each.
2. With regard to Rebecca’s research question, what would you conclude
from these results about the importance of distance travelled as opposed
to type of vehicle used?
3. Comment on Rebecca’s analysis and the assumptions she has made.
What are the implications of this for the validity and reliability of her
4. What data would Rebecca need and what sort of analysis could Rebecca
do in order to take her research further as her project tutor suggests, such
as seeing how different aspects of the supply chain affect total carbon
emissions for different products within that supply chain?
Bimpeh, M., Djokoto, E., Doe, H. and Jequier, R. (2006) Life Cycle Assessment
(LCA) of the Production of Home made and Industrial Bread in Sweden.
KTH, Life Cycle Assessment Course (1N1800), May 2006.
Coley D., Howard M. and Winter M. (2009) Local food, food miles and carbon
emissions: A comparison of farm shop and mass distribution approaches,
Food Policy 34(2), pp. 150–155.
Defra (2010) Guidelines to Defra/DECC’s GHG Conversion Factors for Company
Reporting. Produced by AEA for the Department of Energy and Climate
Change (DECC) and the Department for Environment, Food and Rural Affairs
(Defra). Version 1.2.1 FINAL. Updated: 06/10/2010.
Oglethorpe, D.R. (2010) Optimising economic, environmental and social
objectives: A goal programming approach in the food sector, Environment &
Planning A, 42(5), pp. 1239–1254.
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