building energy modeling: the long-term evolution of building … · 2014. 9. 30. · building...
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Building Energy Modeling: the Long-Term Evolution of Building Energy Consumption in
China and India and the Influence of S i i d Cli t Ch H tiSocioeconomic and Climate Change on Heating
and Cooling Demands
Jiyong Eom, Yuyu Zhou, Vaibhav Chaturvedi, Leon Clarke, Son H. Kim, Page Kyle, Pralit Patel
Joint Global Change Research Institute
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Presentation Outline
Modeling Building Energy UseModeling Building Energy UseThe Long-term Evolution of China’s Building Energy Use The Long-term Evolution of India’s Building Energy Use g g gyPopulation Weighted HDD/CDDCoupling Climate Change and Population Migration with p g g p gBuilding Energy UseAssessing the Long-term Impact of China Building Code
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Modeling Building Energy Use:
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Overview of Building Energy Use in China
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China Buildings’ Energy Consumption (2005)
Note: 190 million households in urban183 million households in rural in 2005
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The Detailed Model for Building Energy Use in China
Energy Service Demands
Urban population
Urban Building
HeatingCoolingWH&C
End-Use Technologies
Urbanization FloorspaceExpansion
Delivered Fuels
SocioeconomicAssumption
ChinaPopulation
GDP
populationGDP
Rural
Building Floorspace
Rural
WH&CLighting
Appliance
HeatingCooling
FurnaceBoiler
Heat pumpDistrict heat
ACCooker
CoalGasOilRural
populationGDP
RuralBuilding
Floorspace
CoolingWH&C
LightingAppliance
H ti
CookerWater heaterIncandescentFluorescentSolidstate
Lamp
OilHeat
ElectricityBiomass
Trad. biomass
Commercial Building
Floorspace
HeatingCoolingWH&C
LightingEquipment
LampAppliancesEquipment
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1. How should urban/rural population change over time?
Modeling Challenges
Energy Service Demands
Urban population
Urban Building
HeatingCoolingWH&C
End-Use Technologies
Urbanization FloorspaceExpansion
Delivered Fuels
SocioeconomicAssumption
ChinaPopulation
GDP
populationGDP
Rural
Building Floorspace
Rural
WH&CLighting
Appliance
HeatingCooling
FurnaceBoiler
Heat pumpDistrict heat
ACCooker
CoalGasOilRural
populationGDP
RuralBuilding
Floorspace
CoolingWH&C
LightingAppliance
H ti
CookerWater heaterIncandescentFluorescentSolidstate
Lamp
OilHeat
ElectricityBiomass
Trad. biomass
Commercial Building
Floorspace
HeatingCoolingWH&C
LightingEquipment
LampAppliancesEquipment
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1. How should urban/rural population change over time?
Modeling Challenges
2. How to build a reasonable floor space expansion model?
Energy Service Demands
Urban population
Urban Building
HeatingCoolingWH&C
End-Use Technologies
Urbanization FloorspaceExpansion
Delivered Fuels
SocioeconomicAssumption
ChinaPopulation
GDP
populationGDP
Rural
Building Floorspace
Rural
WH&CLighting
Appliance
HeatingCooling
FurnaceBoiler
Heat pumpDistrict heat
ACCooker
CoalGasOilRural
populationGDP
RuralBuilding
Floorspace
CoolingWH&C
LightingAppliance
H ti
CookerWater heaterIncandescentFluorescentSolidstate
Lamp
OilHeat
ElectricityBiomass
Trad. biomass
Commercial Building
Floorspace
HeatingCoolingWH&C
LightingEquipment
LampAppliancesEquipment
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1. How should urban/rural population change over time?
Modeling Challenges
2. How to build a reasonable floor space expansion model?
3a. How should energy service demands per unit of floor space vary with income and prices?
Energy Service Demands
Urban population
Urban Building
HeatingCoolingWH&C
End-Use Technologies
Urbanization FloorspaceExpansion
Delivered Fuels
SocioeconomicAssumption
ChinaPopulation
GDP
populationGDP
Rural
Building Floorspace
Rural
WH&CLighting
Appliance
HeatingCooling
FurnaceBoiler
Heat pumpDistrict heat
ACCooker
CoalGasOilRural
populationGDP
RuralBuilding
Floorspace
CoolingWH&C
LightingAppliance
H ti
CookerWater heaterIncandescentFluorescentSolidstate
Lamp
OilHeat
ElectricityBiomass
Trad. biomass
Commercial Building
Floorspace
HeatingCoolingWH&C
LightingEquipment
LampAppliancesEquipment
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1. How should urban/rural population change over time?
Modeling Challenges
2. How to build a reasonable floor space expansion model?
3a. How should energy service demands per unit of floor space vary with income and prices?
3b How to define urban/rural preferences for individual energy service demands and fuels?3b. How to define urban/rural preferences for individual energy service demands and fuels?
Energy Service Demands
Urban population
Urban Building
HeatingCoolingWH&C
End-Use Technologies
Urbanization FloorspaceExpansion
Delivered Fuels
SocioeconomicAssumption
ChinaPopulation
GDP
populationGDP
Rural
Building Floorspace
Rural
WH&CLighting
Appliance
HeatingCooling
FurnaceBoiler
Heat pumpDistrict heat
ACCooker
CoalGasOilRural
populationGDP
RuralBuilding
Floorspace
CoolingWH&C
LightingAppliance
H ti
CookerWater heaterIncandescentFluorescentSolidstate
Lamp
OilHeat
ElectricityBiomass
Trad. biomass
Commercial Building
Floorspace
HeatingCoolingWH&C
LightingEquipment
LampAppliancesEquipment
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Modeling Urbanization in China
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Urbanization in China• We specify a relationship between urbanization rate and total income
Based on parameters obtained by regressing data from national statistics and UN urbanization prospect (by 2050)
• Urbanization mostly occurring between 2005-2050 and ultimately reaching 80% in China by the end of the century.
Projected urbanization in China and otherProjected urbanization in China and other countries’ historical urbanization
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Demand for Floor space
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The approach to Floor Space ExpansionThis is a very idealized approach that has some weaknesses when applied to floorspace.
IDttDt IPkDemand S
tSt PkSupply
e [$
/ m
2 ]
p
Urban / rural/commercial floor space expansion is not inter-related.
r spa
ce p
rice
Demand increases with income
Households’ preferences for floor space depends only on where they are currently located.
Floo
r
There is no noticeable lag in market adjustment to price and income changes. Steady decline in household
Floor space per capita [m2 per capita]
Steady decline in household size is not an important factor.There is no sizeable technical changes in building floorspace service production.
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International Comparison of Residential Building Floor SpaceBuilding Floor Space
2095
2005
2050
2095
China projected2005
China 1985-2004
Per capita residential floor space for selected countries (1990‐2004, IEA) vs. our projections for China (2005‐2095)
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Demand for Building Services
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Demand for Space Heating Service [GJ-output/m2] :
Modeling the Change in Service DemandsDemand for Space Heating Service [GJ-output/m ] :
tH
t
HtHtttHtH P
YinInternalGaioSurfaceRatShellEffHDDkQ,
,2lnexp1
f S C S G / 2
Space Heating Requirement (satiated demand) Economic Behavior
Demand for Space Cooling Service [GJ-output/m2]
tC
t
CtCtttCtC P
YinInternalGaioSurfaceRatShellEffCDDkQ,
,2lnexp1
Space Cooling Requirement (satiated demand) Economic Behavior
Demand for Other Services (water heating & cooking, lighting, other appliances):
t
iit PYqkQ
2lnexp1
ti P
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How to Represent the Income Effects on Demands: The Case of Chinese Residential BuildingsThe Case of Chinese Residential Buildings
Projected expansion of energy services per unit of floorspaceindexed to their satiation levels (2005-2095)
This shows how households would allocate their income into different energy services given that these services have their own satiation pointsenergy services, given that these services have their own satiation points.
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The Long-Term Evolution of China’s Building Energy Use
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Residential Building Energy Use in ChinaThe three competing drivers: Income growth, fuel substitution, and technology improvement
Residential Energy Use per Unit of Floorspace: International Comparison
Source: Our model calculation, Database for Energy efficiency indicators in Europe, US Building Energy Databook, and OECD stat extracts
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• How would building fuel consumption change over time?
Fast electrification even in the baseline scenario
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
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• How would building fuel consumption change over time? • How might fuel consumption respond to advanced tech programs?
Fast electrification even in the baseline scenarioAdvanced technologies substantially reduces total final energy.g y gyBut, they have nearly no influence on fuel shares
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
Final Energy Use of China Buildings in the Advanced Technology Scenario* [EJ]
* Faster improvement in assumed technical efficiencies than the baseline scenarios with the rate depending on the maturity of the technologies
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• How would building fuel consumption change over time? • How would it respond to a carbon price?
A carbon tax policy reduces total final energy, but not substantially. The tax policy induces the change in fuel shares: more use of p y gelectricity and gas, and less use of coal and district heat
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
Final Energy Use of China Buildings in the Policy Scenario* [EJ]
* Global carbon tax pathway achieving 550ppm CO2 by 2100
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• Why is fuel switching pronounced under a carbon tax policy?
The prices of more carbon-intensive fuels (coal, oil, and biomass) rise faster than the prices of other fuels (electricity and gas)Fuel switching is more attractive under the policy case than the regulatory approach.
Prices of Delivered Fuels in the Baseline Scenario [2005 USD/GJ]
Prices of Delivered Fuels in the Policy Scenario* [2005 USD/GJ]
* Global carbon tax pathway achieving 550ppm CO2 by 2100
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• What would be the impact of a carbon policy on building electrification?
The majority of electrification in China occurs simply because of the income-driven growth in services that use electricity. Price-induced electrification exists in a meaningful way well past mid-century, when carbon prices become very high.
Electrification in Chinese Buildings
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• How would fuel consumption change in urban residential buildings?• How would it respond to a carbon price?
The carbon policy substantially reduces the use of district heat and coal for space heating, while increasing the use of coal gas.
Energy Use per Unit of Floorspacein Urban Residential Buildings in
the Baseline Scenario [GJ/m2]
Energy Use per Unit of Floorspacein Urban Residential Buildings in the Carbon Tax Scenario [GJ/m2]
26
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• How would fuel consumption change in rural residential buildings?• How would it respond to a carbon price?
Service intensification interacts with rapid fuel switching away from traditional biomass, resulting in decreased intensity of energy consumption in rural buildingsconsumption in rural buildings.
Price effects from the carbon policy push to extend the use of traditional bio-energy.
Energy Use per Unit of Floorspacein Rural Residential Buildings in the Baseline Scenario [GJ/m2]
Energy Use per Unit of Floorspacein Rural Residential Buildings in the Carbon Tax Scenario [GJ/m2]
27
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The Long-Term Evolution of India’s Building Energy Use
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India Buildings’ Energy Consumption (2005)
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Final energy consumption by Indian buildings
Very fast electrification: rapid expansion of cooling appliances and lightingrapid expansion of cooling, appliances, and lightingNon-electricity fuel (LPG and coal) used mainly for cooking
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Electrification in Indian Buildings
Similar to China buildings, the majority of electrification in India occurs simply because of the income-driven growth in electricity-p y g yusing services, such as cooling, appliances, and lighting. Price-induced electrification is very small.
60%
70%
30
35
Ener
gy
Baseline[EJ]
Carbon Tax[EJ]
30%
40%
50%
15
20
25
Tota
l Bui
ldin
g E
%]
Baseline[%]
Carbon Tax[%]
10%
20%
30%
5
10
15
tric
ity S
hare
in T [
0%02005 2020 2035 2050 2065 2080 2095
Elec
t
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Population Weighted HDD/CDD
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Method of Population Weighted HDD/CDD
Overview of HDD/CDDOverview of HDD/CDDScenarios DesignM th dMethod
Monthly and annual HDD/CDDP l ti i ht d HDD/CDD Population weighted HDD/CDD
Summary
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Overview of HDD/CDD
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What are HDD and CDD?
Degree-days are essentially the summation of temperature differences from a human comfort level over i Th b h i d d i f dtime. They capture both extremity and duration of outdoor temperatures.
H ti d d (HDD) d li d d (CDD)Heating degree day (HDD) and cooling degree day (CDD) are measured in “degree-days” below (HDD) or above (CDD) the set point.( ) p
In general, a “set point” of 18oC is used for calculating HDD and CDD.
Population weighted HDD/CDD can help understand the aggregated requirement of heating and cooling service across a country or region with heterogeneously distributed population.
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Scenarios Design:g
For Future HDD/CDDFor Future HDD/CDD
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Scenarios design
Emissions Climate models
Population distribution scenarios
scenarios
CCSM3
scenarios
A2r
Ref*
GISS B1HDD
& CDD~ 550
ppmv*
H dl Fi d
CDD
Hadley Fixed
* Reference represented by SRES A2 emissions pathway; 550 ppmvemissions pathway; 550 ppmvscenario represented by the SRES B1 scenario
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Two Emissions Scenarios
A2 Emissions (“Reference”)A2 Emissions ( Reference )High emissions pathway in IPCC SRES scenariosCorresponding to Reference Scenario in GCAM
B1 Emissions (“550ppm”)L i i th i IPCC SRES iLow emissions pathway in IPCC SRES scenariosCorresponding to 550ppm stabilization scenario in GCAM
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Three Climate Models
CCSM (#15) National Center for
Atmospheric Research, USA (Collins et al., 2006)
GISS (#7) NASA/Goddard Institute for
Space Studies USASpace Studies, USA (Russell et al., 2000)
The three climate models were Hadley (#16)
Hadley Centre for Climate Prediction and Source: Maurer et al. Climate model
b d th h d l i
chosen to represent a range of temperature outcomes.
Research/Met Office, UK (Gordon et al., 2000)
based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America. Hydrol. Earth Syst. Sci., 13, 183–194, 2009
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Three population distribution scenarios (A2r B1 Fixed) *N t th t thi i l(A2r, B1, Fixed) *Note that this is only
the population distribution. We are using a single g gpopulation scenario for GCAM analyses
Fixed scenario: we maintain the year 2000 population distribution.
Source: A. Grübler et al. / Technological Forecasting & Social Change 74 (2007) 980–1029
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Step 1:
Annual HDD/CDD Estimation
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HDD/CDD Calculation
Heating Degree Day (HDD)
m
cMonth THDD )18( 12
Monthy HDDHDDday 1
1Month
Monthy
Cooling Degree Day (CDD)
m
12
)18(1
day
cMonth TCDD
1Month
Monthy CDDCDD
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Monthly mean temperature (0 5º 2000 2099)(0.5º, 2000-2099)
Monthly mean temperature data does not fully capture the variation in daily mean temperature.An improvement is needed for annual HDD/CDD estimation.
Monthly Temperature
CCSM GISS Hadley
Ref 550ppm Ref 550ppm Ref 550ppm
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Variance of Daily Temperature(historical daily data 1950 1999)
Observationally-derived baseline data
(historical daily data 1950-1999)
Observationally derived baseline data
Source: Globally Downscaled Climate Data t htt // d / / l b l d t /
30
at http://www.engr.scu.edu/~emaurer/global_data/
10
perature
‐10Temp
Original
‐30
1 61 121 181 241 301 361
Day
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Monthly and Annual HDD/CDD
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Annual HDD 2000-2099 (Ref & CCSM)
High Low
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Step 2:
Weighting HDD and CDD by Population
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Population Data from IIASA
We are focusing on distribution of l ti t t t lpopulation – not on totals.
3 distribution scenarios0.5 degree spatial resolution (same as temperature data)
A2r
Population(1990-2100)10 year Step
B110-year Step
FixedFixed
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IIASA Population Downscaling Method
4 steps to downscaling 11 regions-> 185 countries->sub-national (urban &
rural)->grid
Urban and rural population was disaggregated at national levelnational level
Gravity type model was applied on urban population
49
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Change in Population Distribution (A2r)180°150° E120° E90° E60° E30° E0°30° W60° W90° W120° W150° W 180° 150° E120° E90° E60° E30° E0° 30° W60° W90° W120° W150° W
90°
60° N 60° N
30° N 30° N
0° 0°0 0
30° S 30° S
180° 180° 150° E120° E90° E60° E30° E0° 30° W60° W90° W120° W150° W
±0 5 000 10 0002 500
ratio (populaiton 2100/2000)
±0 5,000 10,0002,500
Kilometers< 0.29
0.29 - 0.36
0.37 - 0.43
0.44 - 0.57
0.58 - 1
1.01 - 1.14
1.15 - 1.99
> 1.99
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Change in Population Distribution (B1)180°150° E120° E90° E60° E30° E0°30° W60° W90° W120° W150° W 180° 150° E120° E90° E60° E30° E0° 30° W60° W90° W120° W150° W
90°
60° N 60° N
30° N 30° N
0° 0°0 0
30° S 30° S
180° 180° 150° E120° E90° E60° E30° E0° 30° W60° W90° W120° W150° W
±0 5 000 10 0002 500
ratio (populaiton 2100/2000)
±0 5,000 10,0002,500
Kilometers< 0.29
0.29 - 0.36
0.37 - 0.43
0.44 - 0.57
0.58 - 1
1.01 - 1.14
1.15 - 1.99
> 1.99
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Population Weighted HDD/CDD
HDDi :Heating degree day in GCAM region i
CDDi :Cooling degree day in GCAM region i
HDDi,j :Heating degree day of cell j in GCAM region i
Pi,j :Annual population of cell j in GCAM region i
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Population Weighted HDD 2000-2100 (CCSM-ref-A2r)
High
Low
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Results
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Global Population Weighted HDD
Ref emissions scenario 550 ppm emissions scenario2900
CCSM‐Ref‐A2
GISS‐Ref‐A2
2900CCSM‐550‐A2
GISS‐550‐A2
2200Hadley‐Ref‐A2
CCSM‐Ref‐B1
GISS‐Ref‐B1
2200Hadley‐550‐A2
CCSM‐550‐B1
GISS‐550‐B1
1500 Hadley‐Ref‐B1
CCSM‐Ref‐Fix
GISS‐Ref‐Fix
1500 Hadley‐550‐B1
CCSM‐550‐Fix
GISS‐550‐Fix
800
GISS Ref Fix
Hadley‐Ref‐Fix 800
GISS 550 Fix
Hadley‐550‐Fix
Increased population in “less-cold” areas
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Global Population Weighted CDD
Ref emissions scenario 550 ppm emissions scenario
5700CCSM‐550‐A2
GISS‐550‐A2
5700CCSM‐Ref‐A2
GISS‐Ref‐A2
4100
4900Hadley‐550‐A2
CCSM‐550‐B1
GISS 550 B14100
4900Hadley‐Ref‐A2
CCSM‐Ref‐B1
GISS R f B1
3300
GISS‐550‐B1
Hadley‐550‐B1
CCSM‐550‐Fix3300
GISS‐Ref‐B1
Hadley‐Ref‐B1
CCSM‐Ref‐Fix
2500
GISS‐550‐Fix
Hadley‐550‐Fix2500
GISS‐Ref‐Fix
Hadley‐Ref‐Fix
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Regional HDD/CDD
Global map of GCAM regions
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Emission Scenarios (Ref & 550ppm)based on CCSM & A2r popbased on CCSM & A2r pop
5000 5000
USA HDD China HDD
3000
4000
USA_CCSM‐ref‐A2r
USA CCSM‐550‐A2r
3000
4000
China_CCSM‐ref‐A2r
China CCSM‐550‐A2r
2000
_
2000
_
3000
4000
China_CCSM‐ref‐A2r
China_CCSM‐550‐A2r
3000
4000
USA_CCSM‐ref‐A2r
USA_CCSM‐550‐A2r
USA CDD China CDD
20002000
10001000
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Climate Models (CCSM, GISS, & Hadley)based on ref and A2r popbased on ref and A2r pop
5000 5000
USA HDD China HDD
3000
4000
USA_CCSM‐ref‐A2r
USA_GISS‐ref‐A2r3000
4000
China_CCSM‐ref‐A2r
China_GISS‐ref‐A2r
2000
USA_HADLEY‐ref‐A2r
2000
China_HADLEY‐ref‐A2r
3000
4000
3000
4000
USA CDD China CDD
1000
2000
USA_CCSM‐ref‐A2r
USA_GISS‐ref‐A2r1000
2000
China_CCSM‐ref‐A2r
China_GISS‐ref‐A2r
0USA_HADLEY‐ref‐A2r
0China_HADLEY‐ref‐A2r
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Population Scenarios (A2r, B1 & Fixed)b d CCSM d fbased on CCSM and ref
50005000
USA HDD China HDD
3000
4000
China_CCSM‐ref‐A2r
China CCSM ref B1
3000
4000
USA_CCSM‐ref‐A2r
USA CCSM ref B1
2000
China_CCSM‐ref‐B1
China_CCSM‐ref‐Fix
2000
USA_CCSM‐ref‐B1
USA_CCSM‐ref‐Fix
3000
4000
China_CCSM‐ref‐A2r
China_CCSM‐ref‐B1
China_CCSM‐ref‐Fix3000
4000
USA_CCSM‐ref‐A2r
USA_CCSM‐ref‐B1
USA_CCSM‐ref‐Fix
USA CDD China CDD
20002000
10001000
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Summary of population weighted HDD/CDD
Emissions: Emission scenarios have important impact on population weighted HDD/CDD, and the difference popu at o e g ted /C , a d t e d e e cebetween emission scenarios increases in mid century.
Climate models: HDD/CDD from different climate models have similar trend, and CCSM generally has medium estimation of HDD/CDD.
Population migration: The population migration has Population migration: The population migration has different impact on population weighted HDD/CDD in different level and in different regions. At global level,
l ti i i “l ld” At i lpopulation may increase in “less-cold” areas. At regional level, population migration shows different impacts (e.g. China and USA).)
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Coupling Climate Change and Population Migration
with Building Energy Use
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China Building Final Energy Use
In the reference scenario, climate change lowers China buildings’ final energy by 6% or less.Stabilization policy reduces the shifts in building energy use.
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Heating/Cooling Energy Use in China (cumulative energy in EJ, 2005‐2095)
Comparison across migration scenariosComparison across climate models p gp
Less heating energy use & more cooling energy use with climate changeMigration decreases heating energy use but increases cooling energy use:
Urban growth and its spillover concentrated in ‘less-cold’ eastern areas
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Changes in Building Final Energy Use in China (cumulative energy in EJ, 2005‐2095)
Less use of direct fossil fuels (coal,Less use of direct fossil fuels (coal, oil, gas, and biomass), less use of district heat, and more use of electricity in the buildings sector.
Regardless of emissions scenarios, reductions in building energy use
di t ib t d i f ilare distributed across various fossil fuels
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Assessing the Long-term Impact of China Building Codes
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Downscaling the China Buildings Sector
Severe Cold
ColdSevere
Cold
Hot Summer
Severe Cold
ColdCold Winter
Temperate
Hot Summer Warm Winter
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The Twelve Buildings Sectors
China
Cold Hot Summer Cold Winter
Hot Summer Warm Winter Severe Cold
Urban Residential
Urban Residential
Urban Residential
Urban Residential
Rural Residential
Rural Residential
Rural Residential
Rural Residential
Commercial Commercial Commercial Commercial
9 ProvincesBeijing, Tianjin,
9 ProvincesShanghai, Jiangsu,
6 Provinces & 2 countries
6 Provinces & 2 countries
68
Hebei, Shanxi, Shandong, Henan, Tibet, Shaanxi, Gansu
Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan
Fujian, Guangdong, Guangxi, Hainan, Guizhou, Yunnan Cambodia, Vietnam
Inner Mongolia, Jilin, Heilongjiang, Qinghai, Xinjiang, Liaoning, Mongolia, DPRK
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Modeling Overview
Climate Change Impact: Heating/Cooling Degree
Global Change
Days by Sub‐Region
Global Change Assessment
Model (GCAM)Building Stock Model Detailed
Historical info: ConstructionBuilding types
d / d d
Building Energy
lModelBuilding
Energy Model
Codes/standardsU value
Results
Base‐year energy consumption by fuel and service
Assumptions about future building
codes, compliance, fuel and serviceand building retrofit
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Building Energy Consumption: The Three Building Shell Efficiency Scenarios (Preliminary Results)Shell Efficiency Scenarios (Preliminary Results)
We constructed a building stock model that specifies building construction, building code development and enforcement, and retrofits and retirement. Based on this, three distinct shell efficiency scenarios were developed. The improvement in building envelope is likely to have a sizeable impact on total building energy consumption in China.The impact varies across climate regions in China
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Current and Future Research
CurrentDevelopment of a Global Building ModelDevelopment of a Global Building ModelMulti-model Detailed assessment of US Building Sector:
Linking to BEND and WRF in Richland
FutureDevelopment of a population migration model linked to income distribution and climate conditiondistribution and climate condition Climate feedback and downscaling