1. Practices by proxy:
Climate, Consumption and
Water
Dr Ben Anderson
Department of Sociology, University of Essex &
Lancaster Environment Centre
ESRC Sustainable Practices Research Group
20 March 2012

2. Practices: Climate, Consumption and Water
Why?
How?
What?
Where next?

3. Why?

4. Water is (going to be) a problem
 Supply problems:
» Locally/regionally scarce
» Climate change effects?
 Energy problems:
» ‘Clean’ water costs and ‘clean’ is a
moving target
 Demand problems:
» 50% used by households
» Drivers not well understood
» Climate change effects?
 Demographic problems
» Increasing single person
households
Source: Environment Agency, 2008

5. Water is (going to be) a problem
 With no ‘behaviour’ change and no flow controls
2050
Source: DEFRA, 2011

6. Micro water: Conceptual Framework
‘habits’
Why people don’t do
‘bodily and mental routines’
what they ‘should’
‘permanent dispositions’
Consumption = f(price + demographics + practices + attitudes) + error
Regulation/ Education
Market/ ?! ? Information
Supply Exhortation
Policy levers
Climate change

7. What is currently unclear…
Consumption
Price Demographics Practices Attitudes
Error
Climate change (uncertainty/things we can’t measure)

8. What is currently unclear…
Consumption
Price Demographics Practices Attitudes
Error
Climate change (uncertainty/things we can’t measure)

9. What is currently unclear…
Consumption
Price Demographics Practices Attitudes
Error
Climate change (uncertainty/things we can’t measure)

10. What is currently unclear…
Consumption
Price Demographics Practices Attitudes
Education
Information
Exhortation?
Error
Climate change (uncertainty/things we can’t measure)

11. What is currently unclear…
Consumption
Price Demographics Practices Attitudes
Error
Climate change (uncertainty/things we can’t measure)

12. How?

13. Data I (Household water demand)
Ideal Proxy (EFS 2002-2009)
water (l/day) £ water/week
Demographics Demographics
Shampoo,soap
Fruit & Veg
detergents
Practices £/week
Tea, coffee, juices Garden products
Price Price
Attitudes Attitudes

14. Data II (Weather/Climate)
 MetOffice Regional Weather records
Weather data http://www.metoffice.gov.uk/climate/uk/
 Linked to
25 £5.05
– household government office region £5.00
– Lagged survey month
20 £4.95
£4.90
 Observed
15 £4.85
£4.80
–
10 Mean rainfall £4.75
£4.70
–5 Number of rain days £4.65
–0
Mean temperature £4.60
£4.55
– Mean sunshine hours
january
february
march
april
may
june
july
august october
september
december
november
Climate data  3 year anomalies
Water £/week Mean rainfall (cm) Mean number
raindays
Mean sunshine Mean temperature
hours (/10)

15. Modelling approach
 2005 prices £7.00 40.00%
 Selection: £6.00 35.00%
30.00%
£5.00
– Have water meter (England) £4.00
25.00%
20.00%
£3.00
15.00%
£2.00 10.00%
£1.00 5.00%
£0.00 0.00%
2002 2003 2004 2005 2006 2007 2008 2009
No water me- Has water % metered
ter meter

16. Modelling approach
 2005 prices All households 39121
 Selection: Metered 11119
– Have water meter (England)
Separate water & 1387
– Pay water & sewerage combined sewerage
Remaining 9732

17. Modelling approach
 2005 prices All households 39121
 Selection: Metered 11119
– Have water meter (England)
Separate water & 1387
– Pay water & sewerage combined sewerage
 Split sample into ‘seasons’
20 £4.90 Remaining 9732
15 £4.85
10 £4.80
5 £4.75
0 £4.70
Winter (Dec – Feb) Spring (Mar – May) Summer (Jun – Aug) Autumn (Sep – Nov)
Water £/week Mean rainfall (cm) Mean number
raindays
Mean sunshine Mean temperature
hours (/10)

18. Modelling approach
 2005 prices
 Selection: Proxy (EFS 2002-2009)
– Have water meter (England)
– Pay water & sewerage combined £ water/week
 Model 1
– Demographics & practices, no weather/ Demographics
climate Fruit & Veg
Shampoo,soap
 Model 2 by season £/week
detergents
– includes lagged weather & 'climate' Garden products
Tea, coffee, juices
 Plus controls:
Price
– Ownership of dishwasher, income,
Climate data Weather data
region, tenure, number rooms, number
of cars, number of earners, Attitudes
accommodation type

19. What?

20. Model 1: Demographics & practices
 Contributions to model
Practices
Illness, age, gender & ethnicity of HRP
Age composition (adults)
Age composition (young people)
R2
change in r2
Cars, earners, employment, composition
Housing type, rooms, tenure
Govt Office Region & Year
Washing machine, dishwasher, income
0 0.05 0.1 0.15 0.2 0.25 0.3

21. Model 1: Demographic effects
N adults 70+
N adults 65-70
N adults 60-65
N adults 45-60
N female adults < 45
N male adults < 45
N single females 16-18
N single males 16-18
N Children 14-16
N Children < 14
-0.8 -0.3 0.2 0.7 1.2
b

22. Model 1: ‘Practices’ effects
 Contributions to model
Plants, flowers, seeds
Lawn mowers
Garden tools
Kitchen gloves/cloths
Detergents/washing powder
Laundry/Laundrettes
Soap/shower gel
Mineral/spring water
Vegetable juices
Fruit juices (incl squash)
Coffee
Tea
Pasta
Rice
Leaf & stem vegetables
Potatoes
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2
b

23. Model 2: Demographics & practices & weather
 Contributions to model (all
seasons)
Weather/climate
Practices
Illness, age, gender & ethnicity of HRP
Age composition (adults)
Age composition (young people) R2
change in r2
Cars, earners, employment, composition
Housing type, rooms, tenure
Govt Office Region
Washing machine, dishwasher, income
0 0.05 0.1 0.15 0.2 0.25 0.3

24. Model 2: Weather effects
 Only in Autumn:
Unusually hot & dry (rain days, 3 year anom)
Unusually hot & dry (rainfall, 3 year anom)
Mean temperature (3 year anom)
Mean temperature
Mean sunshine (3 year anom)
Mean sunshine
Rain days (3 year anom)
Rain days
Mean rainfall (3 year anom)
Mean rainfall
-3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5
b

25. Conclusions
 The practice proxies approach offers value?
 The weather data doesn't?
 Confounding problems?
– Expenditures as proxies?
– Garden/soil type?
– Period of water use?
– Included sewerage costs?
– Poorly matched and coarse grained weather 'regions'?
– Consumer water saving responses to 'dry' weather?

26. Where next?
• Multilevel model?
– Weather data 'clustered'
– But is it worth it?
• More accurate water bill period?
– Closer match to weather
• Better geo-coding?
– More accurate match to weather, soils,
water prices/company
 'Practices' Survey
 Linked to water meter data
 Small area estimates of demand
 Census 2001 – 2011

27. Where next?
• Multilevel model?
– Weather data 'clustered'
– But is it worth it?
• More accurate water bill period?
– Closer match to weather
• Better geo-coding?
– More accurate match to weather, soils,
water prices/company
 'Practices' Survey
 Linked to water meter data
 Small area estimates of demand
 Census 2001 – 2011
 Application to energy demand?

28. Thank you!
• ESRC Sustainable Practices Research Group
• www.sprg.ac.uk/projects-fellowships/patterns-of-water
Contact:
– Ben Anderson (benander@essex.ac.uk)