Anderson, B., Browne, A., and Medd, W., (2012) Practices by proxy: climate, consumption and water. Paper presented at Living Costs and Food Survey user meeting, Tuesday 20 March 2012 at the Royal Statistical Society, London
Practices by Proxy: Climate, Consumption and Water (and troubles with data)Ben Anderson
Please cite as:
Anderson, B (2012) Practices By Proxy: Climate, Consumption and Water, Paper presented at “Can Climate Change Policies Be Fair?”, Royal Statistical Society, July 5th 2012, London
Growth in margin of ecological thought by perezLucie Evers
This chapter discusses the debate around economic growth that emerged in the late 1960s. Concerns about pollution, resource scarcity, and overpopulation led some economists and scientists to argue that perpetual economic growth was unsustainable and exacerbating environmental problems. While this view challenged mainstream economic thought, the concept of sustainable development that emerged politically settled the debate without resolving the underlying issues. The chapter will summarize the growth debate, examine Herman Daly's concept of a steady-state economy as an alternative to growth, and explore Serge Latouche's idea of degrowth. It will compare these perspectives and consider their political feasibility and role for social science.
Diese Studienergebnisse basieren auf zwei umfangreichen Befragungen in den Jahren 2011 und 2012. Ziel war, erstmals valide Informationen zu gewinnen über die Transparenzerwartungen und -einschätzungen von Verbrauchern.
Executive Summary
• 82 Prozent der Deutschen möchten, dass Unternehmen transparenter werden. 80 Prozent wünschen sich sogar strengere gesetzliche Regelungen.
• Verbraucher haben hohe Transparenzerwartungen an Unternehmen. Besonders hoher Handlungsdruck besteht für Unternehmen aus den Branchen Lebensmittel, Energie, Pharma und Banken.
• Verbraucher gehen davon aus, dass transparente Unternehmen nachhaltiger, umweltschonender, sozialer und innovationsfähiger sind als intransparente Unternehmen.
• Für 22 Prozent der Deutschen – die Transparenz-Verfechter – ist Transparenz ein wichtiges Kaufkriterium. Bei 56 Prozent der Befragten hat Transparenz gelegentlich Einfluss auf die Kaufentscheidung.
• Es besteht sowohl ein signifikanter Zusammenhang zwischen Transparenz und Vertrauen als auch zwischen Transparenz und Sympathie (Image).
• Je höher die Bedeutung von Transparenz eingestuft wird,
• desto höhere Erwartungen an nachhaltige Unternehmensführung haben die Befragten.
• Transparenz nach innen steigert in hohem Maße die Mitarbeiterzufriedenheit.
• Volkswagen wird als das transparenteste Unternehmen Deutschlands wahrgenommen, die Deutsche Telekom als das intransparenteste.
• Die Deutschen attestieren allen politischen Parteien großen Nachholbedarf in Sachen Transparenz.
Water 'Practices' - Preliminary ThoughtsBen Anderson
Slides from a CRESI seminar on the ESRC's new Sustainable Practices Research Group http://cresi.wordpress.com/2011/01/27/seminar-sustainable-practices/
Ben Anderson's slides from a CRESI seminar on the ESRC’s new Sustainable Practices Research Group http://cresi.wordpress.com/2011/01/27/seminar-sustainable-practices/
This document provides statistics and benchmarks for evaluating membership programs. It defines common statistical terms used to analyze fundraising results and compares performance across industries. The document recommends comparing results to external indicators like consumer confidence indexes. Finally, it analyzes fundraising metrics for arts and culture organizations to benchmark performance against similar nonprofits.
Practices by Proxy: Climate, Consumption and Water (and troubles with data)Ben Anderson
Please cite as:
Anderson, B (2012) Practices By Proxy: Climate, Consumption and Water, Paper presented at “Can Climate Change Policies Be Fair?”, Royal Statistical Society, July 5th 2012, London
Growth in margin of ecological thought by perezLucie Evers
This chapter discusses the debate around economic growth that emerged in the late 1960s. Concerns about pollution, resource scarcity, and overpopulation led some economists and scientists to argue that perpetual economic growth was unsustainable and exacerbating environmental problems. While this view challenged mainstream economic thought, the concept of sustainable development that emerged politically settled the debate without resolving the underlying issues. The chapter will summarize the growth debate, examine Herman Daly's concept of a steady-state economy as an alternative to growth, and explore Serge Latouche's idea of degrowth. It will compare these perspectives and consider their political feasibility and role for social science.
Diese Studienergebnisse basieren auf zwei umfangreichen Befragungen in den Jahren 2011 und 2012. Ziel war, erstmals valide Informationen zu gewinnen über die Transparenzerwartungen und -einschätzungen von Verbrauchern.
Executive Summary
• 82 Prozent der Deutschen möchten, dass Unternehmen transparenter werden. 80 Prozent wünschen sich sogar strengere gesetzliche Regelungen.
• Verbraucher haben hohe Transparenzerwartungen an Unternehmen. Besonders hoher Handlungsdruck besteht für Unternehmen aus den Branchen Lebensmittel, Energie, Pharma und Banken.
• Verbraucher gehen davon aus, dass transparente Unternehmen nachhaltiger, umweltschonender, sozialer und innovationsfähiger sind als intransparente Unternehmen.
• Für 22 Prozent der Deutschen – die Transparenz-Verfechter – ist Transparenz ein wichtiges Kaufkriterium. Bei 56 Prozent der Befragten hat Transparenz gelegentlich Einfluss auf die Kaufentscheidung.
• Es besteht sowohl ein signifikanter Zusammenhang zwischen Transparenz und Vertrauen als auch zwischen Transparenz und Sympathie (Image).
• Je höher die Bedeutung von Transparenz eingestuft wird,
• desto höhere Erwartungen an nachhaltige Unternehmensführung haben die Befragten.
• Transparenz nach innen steigert in hohem Maße die Mitarbeiterzufriedenheit.
• Volkswagen wird als das transparenteste Unternehmen Deutschlands wahrgenommen, die Deutsche Telekom als das intransparenteste.
• Die Deutschen attestieren allen politischen Parteien großen Nachholbedarf in Sachen Transparenz.
Water 'Practices' - Preliminary ThoughtsBen Anderson
Slides from a CRESI seminar on the ESRC's new Sustainable Practices Research Group http://cresi.wordpress.com/2011/01/27/seminar-sustainable-practices/
Ben Anderson's slides from a CRESI seminar on the ESRC’s new Sustainable Practices Research Group http://cresi.wordpress.com/2011/01/27/seminar-sustainable-practices/
This document provides statistics and benchmarks for evaluating membership programs. It defines common statistical terms used to analyze fundraising results and compares performance across industries. The document recommends comparing results to external indicators like consumer confidence indexes. Finally, it analyzes fundraising metrics for arts and culture organizations to benchmark performance against similar nonprofits.
Using Time Use Data To Trace 'Energy Practices' Through TimeBen Anderson
The document discusses trends in energy demand (DEMAND) over time based on time use survey data from 1974 to 2005. It analyzes 10 activity classes and shows changes over time, with declines in activities like travel, cooking and eating, and increases in media use. Food preparation trends are examined in more detail, showing peaks shifting from the evening to the late morning on weekends. The analysis of time use data provides insights into how energy-demanding activities and practices have changed over the past several decades.
Modeling Water Demand in Droughts (in England & Wales)Ben Anderson
This document describes an agent-based microsimulation model for estimating domestic water demand under drought conditions in the UK. The model simulates individual households and factors that influence water usage, such as household attributes, appliances, practices, pricing, and drought interventions. Preliminary results show that including drought responses can reduce total water demand by 5% compared to not including responses. Further development of the model will add more influencing factors and link it to drought forecasts to better estimate future water demand scenarios.
A Social Practices-based Microsimulation Model for Estimating Domestic Hot Wa...Ben Anderson
This document describes an IMPETUS microsimulation model developed to estimate domestic hot water demand in the UK. The model takes into account factors like weather, demographics, technology changes, and personal water usage behaviors. It aims to better address uncertainties around climate change, social practices, and responses to water and energy efficiency measures. Heating water accounts for over half of residential water usage in the UK and is the second largest energy use in homes. The model suggests that installing efficient showerheads can substantially reduce both water consumption and energy demand. Future work will assess the impacts of other water efficiency interventions and behavior changes on energy usage.
This document discusses a project called Solent Achieving Value from Efficiency (SAVE) that aims to reduce energy usage through LED lighting upgrades and monitor the financial and energy savings. It will conduct a trial evaluation including surveys and collecting energy usage data to analyze the results and share updates.
SAVE: A large scale randomised control trial approach to testing domestic ele...Ben Anderson
The document describes a study that tested demand response interventions in the UK using a large randomized controlled trial approach. Over 4,000 households were recruited and randomly assigned to control and intervention groups. Initial results found that messages encouraging shifting electricity use away from peak hours had little impact, while the addition of a financial incentive reduced consumption by up to 5% in the targeted hours. The study is using high frequency electricity consumption data and modeling techniques to analyze flexibility at a local level, which could help target interventions and inform network investment decisions.
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Dr. Ben Anderson presents on hunting for energy demanding practices using big and medium sized data. He discusses linking transactional energy use data to surveys and time use studies to identify household practices that influence demand, such as cooking and cleaning routines. New sources of data like smart meter traces are also explored for revealing rhythms of demand. Challenges include scaling up micro-level insights and taking post-disciplinary approaches to analyze data of different sizes.
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
Census2022: Extracting value from domestic consumption data in a postcensus eraBen Anderson
This document discusses a project called Census2022 that aims to extract value from domestic consumption data from smart meters in a post-census era. It details how smart meter data at high temporal resolution could be aggregated to small geographic areas to generate household statistics and indicators. The document then describes a study conducted with smart meter-like household electricity consumption data from 180 UK homes. Preliminary analysis of load profile indicators showed differences between households of varying sizes and employment statuses. However, more complex models are needed to better predict household characteristics from electricity use alone. Future steps involve accessing larger datasets and creating novel energy-based social indicators.
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
This document summarizes research on the distribution of domestic energy technologies in Great Britain from 2008 to 2011. It finds that adopters of these technologies tend to be home-owning residents of detached, rural homes who report environmentally friendly views and actions. Those seriously considering adoption also tend to have higher energy spending. Rejecters of these technologies are more often urban renters living in non-detached housing who report less environmentally friendly attitudes. The research finds some differences between adopters of solar PV and solar thermal technologies and highlights equity issues in the uneven distribution of benefits from these technologies.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Using Time Use Data To Trace 'Energy Practices' Through TimeBen Anderson
The document discusses trends in energy demand (DEMAND) over time based on time use survey data from 1974 to 2005. It analyzes 10 activity classes and shows changes over time, with declines in activities like travel, cooking and eating, and increases in media use. Food preparation trends are examined in more detail, showing peaks shifting from the evening to the late morning on weekends. The analysis of time use data provides insights into how energy-demanding activities and practices have changed over the past several decades.
Modeling Water Demand in Droughts (in England & Wales)Ben Anderson
This document describes an agent-based microsimulation model for estimating domestic water demand under drought conditions in the UK. The model simulates individual households and factors that influence water usage, such as household attributes, appliances, practices, pricing, and drought interventions. Preliminary results show that including drought responses can reduce total water demand by 5% compared to not including responses. Further development of the model will add more influencing factors and link it to drought forecasts to better estimate future water demand scenarios.
A Social Practices-based Microsimulation Model for Estimating Domestic Hot Wa...Ben Anderson
This document describes an IMPETUS microsimulation model developed to estimate domestic hot water demand in the UK. The model takes into account factors like weather, demographics, technology changes, and personal water usage behaviors. It aims to better address uncertainties around climate change, social practices, and responses to water and energy efficiency measures. Heating water accounts for over half of residential water usage in the UK and is the second largest energy use in homes. The model suggests that installing efficient showerheads can substantially reduce both water consumption and energy demand. Future work will assess the impacts of other water efficiency interventions and behavior changes on energy usage.
This document discusses a project called Solent Achieving Value from Efficiency (SAVE) that aims to reduce energy usage through LED lighting upgrades and monitor the financial and energy savings. It will conduct a trial evaluation including surveys and collecting energy usage data to analyze the results and share updates.
SAVE: A large scale randomised control trial approach to testing domestic ele...Ben Anderson
The document describes a study that tested demand response interventions in the UK using a large randomized controlled trial approach. Over 4,000 households were recruited and randomly assigned to control and intervention groups. Initial results found that messages encouraging shifting electricity use away from peak hours had little impact, while the addition of a financial incentive reduced consumption by up to 5% in the targeted hours. The study is using high frequency electricity consumption data and modeling techniques to analyze flexibility at a local level, which could help target interventions and inform network investment decisions.
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Dr. Ben Anderson presents on hunting for energy demanding practices using big and medium sized data. He discusses linking transactional energy use data to surveys and time use studies to identify household practices that influence demand, such as cooking and cleaning routines. New sources of data like smart meter traces are also explored for revealing rhythms of demand. Challenges include scaling up micro-level insights and taking post-disciplinary approaches to analyze data of different sizes.
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
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Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
Census2022: Extracting value from domestic consumption data in a postcensus eraBen Anderson
This document discusses a project called Census2022 that aims to extract value from domestic consumption data from smart meters in a post-census era. It details how smart meter data at high temporal resolution could be aggregated to small geographic areas to generate household statistics and indicators. The document then describes a study conducted with smart meter-like household electricity consumption data from 180 UK homes. Preliminary analysis of load profile indicators showed differences between households of varying sizes and employment statuses. However, more complex models are needed to better predict household characteristics from electricity use alone. Future steps involve accessing larger datasets and creating novel energy-based social indicators.
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
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Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
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Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
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This document summarizes research on the distribution of domestic energy technologies in Great Britain from 2008 to 2011. It finds that adopters of these technologies tend to be home-owning residents of detached, rural homes who report environmentally friendly views and actions. Those seriously considering adoption also tend to have higher energy spending. Rejecters of these technologies are more often urban renters living in non-detached housing who report less environmentally friendly attitudes. The research finds some differences between adopters of solar PV and solar thermal technologies and highlights equity issues in the uneven distribution of benefits from these technologies.
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Practices by proxy: Climate, Consumption and Water
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
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)
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
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)
Editor's Notes
Climate change effects on supply side - fewer rain days, heavier rain - can’t capture, supply less predictable so more storage needed Demand side - warmer summers -> more domestic (bathing) & gardening use?
We can’t directly observe practices - and no data (yet) does this and also collects all the other data we need. The EFS offers a way to do this by proxy £ water expenditure/week n people, age etc Proxies for practices Shampoo, soap, detergents, gardening etc Bottled water Garden products ?