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Modelling retrofit changes to the housing stock:

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Presentation on theme: "Modelling retrofit changes to the housing stock:"— Presentation transcript:

1 Modelling retrofit changes to the housing stock:
simulating decision making and capturing geographic variation Dr Timothy Lee @MyBCU

2 Introduction Challenging 2050 target
80% reduction in CO2 emissions Housing accounts for c.28% of current emissions Therefore changes needed to existing housing if target is to be met Stock models projecting uptake of energy saving measures can aid planning towards the 2050 target @MyBCU

3 Shortcomings of current modelling
No geographic variation in model stocks Eg: City centre flats have different retrofit options to off-gas rural properties Uptake rates of retrofit measures are imposed No consideration of the decision making of the dwelling owners who will actually drive the uptake of measures and therefore the energy and CO2 savings @MyBCU

4 Geographically based housing stock model
Develop a test model for the North East of England (1.1 million homes) Model dwelling stock variation across the region Output area (OA) (c.150 homes) -> Lower level super output area (LSOA) (c.700) -> Medium level super output area (MSOA) (c. 3500) -> Local authority (LA) (c. 80,000) @MyBCU

5 Housing data English Housing Survey data on 935 dwellings in the North East Provides sufficient data for calculation of energy demand Decile IMD rating and 4 level ruralness scale Census Data - Output Area (OA) Built form/detachment IMD Decile Ruralness Heating fuel type @MyBCU

6 Geographic energy data
DECC: Experimental data for gas and electricity use at LSOA level Data for gas and electricity use at MSOA level and LA level 1657 LSOAs in North East in 2011 Census 341 MSOAs in North East in 2011 Census 12 LAs in North East @MyBCU

7 Geographic model results
@MyBCU

8 Understanding and Modelling Decision Making
Retrofit measures only installed when owners decide to install Owner-occupiers Energy Saving Trust (2009) Element Energy (2008) Discrete choice survey data Multiple Criteria Decision Making Model Agent based model @MyBCU

9 Understanding and Modelling Decision Making
Simple additive weighting Available technologies: Fabric Improvements, Heating System Upgrades, Renewable Energy Technologies Considers: Price, Saving, Maintenance, Disruption, Subsidy, Taxation, Recommendation @MyBCU

10 Adoption curves @MyBCU

11 Next steps Improve behavioural understanding
Expand to all tenure types Integrate agents with geographic model @MyBCU

12 Thank you Any questions? timothy.lee@bcu.ac.uk @MyBCU


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