Post on 14-Dec-2015
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