The scheduling and timing of (food) shopping journeys: implications for

transport energy demand

Jillian Anable & Giulio Mattioli

Centre for Transport Research, University of Aberdeen

The ‘taming of the few’(Brand & Boardman, 2008; Brand & Preston, 2010)

«Policy needs to target the gross polluters, i.e. certain subgroups of the population who are responsible for a disproportionally large share of total emissions. Policy has to seek out these differences, identify the causes and target these causes directly»

(Brand & Boardman, 2008, p.234)

• Own survey (Oxfordshire, 2005, N=456 individuals), methodology for profiling annual GHG emissions from personal travel at the disaggregate level

• Air and car travel dominate GHG emissions. Public transport insignificant overall• ‘60-20 rule’ in unequal distribution of transport-related GHG emissions: 60% of

emissions produced by 20% of the population. Valid across units & scale of analysis• Emission levels significantly influenced by socio-demographic factors (income,

activity, age, household structure & car availiability)• However «need for an alternative or complimentary segmentation»

Our approach

• Focus on ‘what people do’ / ‘what energy is for’ (Shove & Walker, 2014). Specific practice: food shopping

• Use NTS 2002-2010 travel week diary data at the household level

• Focus on top 20% of weekly household car driver distance within different types of area (control for built environment effect)

• Estimate CO2 emissions (2010 DECC’s GHG Conversion Factors)

• Move beyond averages, look for variety within the top 20%. Cluster households according to their weekly food shopping travel behaviour

• Focus on frequency / timing of food shopping travel (cfr. Walker, 2014)

Why food shopping?

• It accounts for a substantial amount of travel (shopping = 20% of trips – 32% on Saturday! - 48% of these are for food) and CO2 emissions (shopping = 12% of travel-related CO2 emissions, 37% of these are for food shopping)

• It has become more car intensive over time in the UK: from 2 to 12 minutes of car travel for each episode of ‘purchase of goods’ between 1983 and 2005 (MTUS data analysis). This increase is:• stronger than for other activities• virtually absent in other countries (NL, USA) in the same period

• It is a frequent activity (94% at least once every 7 days, Bhat et al., 2004)• enables to use the NTS 7-day travel diary to profile households based on

their food shopping travel behaviour + estimate CO2 emissions

Top 20% analyis subsample

70.4%

64.1%

ClusteringInput variables1 Distance Percentile of car driver distance travelled (within type of area)

2 Concentration % of distance accounted for by longest trip

3 Frequency Total number of car driver trips in travel week diary

4 Alternative modes % of trips by modes other than car driver / passenger

5 Shopping intensity (distance) % of total household car driver distance accounted for by food shopping

6 Shopping intensity (time) % of total household travel time accounted for by food shopping

4 cluster solutionSingle long

distance trip

Frequent

shopping

Shopping

intensive week

Long distance

trip & alternatives

Cluster 1 Cluster 2 Cluster 3 Cluster 4

Size (%) 44.9% 37.1% 11.7% 7.3%

Distance (percentile) 36 64 64 43

Concentration 50% 28% 34% 49%

Frequency 3.3 6.8 5.5 3.3

Alternatives 0.4% 1.3% 2.3% 40.3%

Shopping intensity – distance 12% 16% 57% 17%

Shopping intensity – travel time 9% 13% 43% 16%

4 cluster solutionSingle long

distance trip

Frequent

shopping

Shopping

intensive

Long distance

trip & alternatives

Cluster 1 Cluster 2 Cluster 3 Cluster 4

Size (%) 44.9% 37.1% 11.7% 7.3%

Distance (percentile) 36 64 64 43

Concentration 50% 28% 34% 49%

Frequency 3.3 6.8 5.5 3.3

Alternatives 0.4% 1.3% 2.3% 40.3%

Shopping intensity – distance 12% 16% 57% 17%

Shopping intensity – travel time 9% 13% 43% 16%

Socio-demographic profile

Conclusion – policy implications

• Frequency (not just distance) as a problem. Concentration of trips and/or substitution with online shopping as policy goals?

• ‘Gross polluters’ need to be targeted, but within this need to look for ‘quick wins’: Cluster 3 might be the most susceptible for substitution of some trips by home shopping or more local trips or non-car modes

• No strong impact of accessibility: built environment matters, but it is only part of the story

CLUSTER 3: SHOPPING INTENSIVE

• mostly pensioner, poorer and smaller households...

• ...but very car reliant for food shopping

• the most travel– and carbon–intensive travel patterns, mostly due to high frequency…

• ...but not gross polluters overall

• fast-growing segment due to ageing car mobile population

• challenge for equitable policies

Thank you for your attention!

www.demand.ac.uk

j.anable@abdn.ac.uk

giulio.mattioli@abdn.ac.uk