building energy modeling: the long-term evolution of building … · 2014-09-30 · building energy...
TRANSCRIPT
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Jiyong Eom, Yuyu Zhou, Vaibhav Chaturvedi, Leon Clarke,
Son H. Kim, Page Kyle, Pralit Patel
Joint Global Change Research Institute
Building Energy Modeling: the Long-Term Evolution of Building Energy Consumption in
China and India and the Influence of Socioeconomic and Climate Change on Heating
and Cooling Demands
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Presentation Outline
! Modeling Building Energy Use ! The Long-term Evolution of China’s Building Energy Use ! The Long-term Evolution of India’s Building Energy Use ! Population Weighted HDD/CDD ! Coupling Climate Change and Population Migration with
Building Energy Use ! Assessing 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 urban 183 million households in rural in 2005
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Energy Service Demands
Commercial Building
Floorspace
China Population
GDP
Urban population
GDP
Rural population
GDP
Urban Building
Floorspace
Rural Building
Floorspace
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Equipment
Furnace Boiler
Heat pump District heat
AC Cooker
Water heater Incandescent Fluorescent Solidstate
Lamp Appliances Equipment
Coal Gas Oil
Heat Electricity Biomass
Trad. biomass
End-Use Technologies
Urbanization Floorspace Expansion
Delivered Fuels
Socioeconomic Assumption
The Detailed Model for Building Energy Use in China
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1. How should urban/rural population change over time?
Energy Service Demands
Commercial Building
Floorspace
China Population
GDP
Urban population
GDP
Rural population
GDP
Urban Building
Floorspace
Rural Building
Floorspace
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Equipment
Furnace Boiler
Heat pump District heat
AC Cooker
Water heater Incandescent Fluorescent Solidstate
Lamp Appliances Equipment
Coal Gas Oil
Heat Electricity Biomass
Trad. biomass
End-Use Technologies
Urbanization Floorspace Expansion
Delivered Fuels
Socioeconomic Assumption
Modeling Challenges
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1. How should urban/rural population change over time?
2. How to build a reasonable floor space expansion model?
Energy Service Demands
Commercial Building
Floorspace
China Population
GDP
Urban population
GDP
Rural population
GDP
Urban Building
Floorspace
Rural Building
Floorspace
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Equipment
Furnace Boiler
Heat pump District heat
AC Cooker
Water heater Incandescent Fluorescent Solidstate
Lamp Appliances Equipment
Coal Gas Oil
Heat Electricity Biomass
Trad. biomass
End-Use Technologies
Urbanization Floorspace Expansion
Delivered Fuels
Socioeconomic Assumption
Modeling Challenges
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1. How should urban/rural population change over time?
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
Commercial Building
Floorspace
China Population
GDP
Urban population
GDP
Rural population
GDP
Urban Building
Floorspace
Rural Building
Floorspace
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Equipment
Furnace Boiler
Heat pump District heat
AC Cooker
Water heater Incandescent Fluorescent Solidstate
Lamp Appliances Equipment
Coal Gas Oil
Heat Electricity Biomass
Trad. biomass
End-Use Technologies
Urbanization Floorspace Expansion
Delivered Fuels
Socioeconomic Assumption
Modeling Challenges
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1. How should urban/rural population change over time?
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?
Energy Service Demands
Commercial Building
Floorspace
China Population
GDP
Urban population
GDP
Rural population
GDP
Urban Building
Floorspace
Rural Building
Floorspace
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Appliance
Heating Cooling WH&C
Lighting Equipment
Furnace Boiler
Heat pump District heat
AC Cooker
Water heater Incandescent Fluorescent Solidstate
Lamp Appliances Equipment
Coal Gas Oil
Heat Electricity Biomass
Trad. biomass
End-Use Technologies
Urbanization Floorspace Expansion
Delivered Fuels
Socioeconomic Assumption
Modeling Challenges
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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 other countries’ historical urbanization
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Demand for Floor space
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IDttDt IPkDemand εε= S
tSt PkSupply ε=
Floo
r spa
ce p
rice
[$ /
m2 ]
Floor space per capita [m2 per capita]
Demand increases with income
This is a very idealized approach that has some weaknesses when applied to floorspace.
The approach to Floor Space Expansion
! Urban / rural/commercial floor space expansion is not inter-related.
! Households’ preferences for floor space depends only on where they are currently located.
! There is no noticeable lag in market adjustment to price and income changes.
! 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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Per capita residen-al floor space for selected countries (1990-‐2004, IEA) vs. our projec-ons for China (2005-‐2095)
China 1985-2004
China projected 2005
2050
2095
International Comparison of Residential Building Floor Space
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Demand for Building Services
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Demand for Space Heating Service [GJ-output/m2] :
( )⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛⋅−−⋅−⋅⋅⋅=
tH
t
HtHtttHtH P
YinInternalGaioSurfaceRatShellEffHDDkQ,
,2lnexp1
αλ
Demand for Space Cooling Service [GJ-output/m2]
Demand for Other Services (water heating & cooking, lighting, other appliances):
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛⋅−−⋅=
t
t
iiit P
YqkQα2lnexp1
( )⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛⋅−−⋅+⋅⋅⋅=
tC
t
CtCtttCtC P
YinInternalGaioSurfaceRatShellEffCDDkQ,
,2lnexp1
αλ
Space Heating Requirement (satiated demand) Economic Behavior
Space Cooling Requirement (satiated demand) Economic Behavior
Modeling the Change in Service Demands
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How to Represent the Income Effects on Demands: The Case of Chinese Residential Buildings
Projected expansion of energy services per unit of floorspace indexed 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 points.
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How to Model Traditional Biomass? ! In 2005, traditional biomass (TB) accounted for 84% and 73% of energy used
by rural households in China and India, respectively. ! Traditional biomass use in rural areas is assumed to be gradually phased out
(No market price but potentially huge non-market price). ! Full cost of TB-delivered energy service
= capital cost of TB-using equipment + time cost of collecting TB
Development of the unit cost of space heating service delivered by traditional biomass with respect to its collecting time (China)
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The Long-Term Evolution of China’s Building Energy Use
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Source: Our model calculation, Database for Energy efficiency indicators in Europe, US Building Energy Databook, and OECD stat extracts
Residential Energy Use per Unit of Floorspace : International Comparison
Residential Building Energy Use in China ! The three competing drivers:
Income growth, fuel substitution, and technology improvement
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China’s Residential Building Energy Use per Unit of Floorspace by Service
22
! Urban and rural energy use patterns are different because of their differences in (1) Fuel options and associated technology efficiencies (2) Urban-to-rural income gap (3) Preferences for energy services
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• How would building fuel consumption change over time?
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
! Fast electrification even in the baseline scenario
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• How would building fuel consumption change over time? • How might fuel consumption respond to advanced tech programs?
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
! Fast electrification even in the baseline scenario ! Advanced technologies substantially reduces total final energy. ! But, they have nearly no influence on fuel shares
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?
* Global carbon tax pathway achieving 550ppm CO2 by 2100
Final Energy Use of China Buildings in the Baseline Scenario [EJ]
Final Energy Use of China Buildings in the Policy Scenario* [EJ]
! A carbon tax policy reduces total final energy, but not substantially. ! The tax policy induces the change in fuel shares: more use of
electricity and gas, and less use of coal and district heat
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• Why is fuel switching pronounced under a carbon tax policy?
* Global carbon tax pathway achieving 550ppm CO2 by 2100
Prices of Delivered Fuels in the Baseline Scenario [2005 USD/GJ]
Prices of Delivered Fuels in the Policy Scenario* [2005 USD/GJ]
! 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.
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• What would be the impact of a carbon policy on building electrification?
Electrification in Chinese Buildings
! 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.
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28
• How would fuel consumption change in urban residential buildings? • How would it respond to a carbon price?
Energy Use per Unit of Floorspace in Urban Residential Buildings in
the Baseline Scenario [GJ/m2]
! 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 Floorspace in Urban Residential Buildings in the Carbon Tax Scenario [GJ/m2]
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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 buildings.
! Price effects from the carbon policy push to extend the use of traditional bio-energy.
Energy Use per Unit of Floorspace in Rural Residential Buildings in
the Baseline Scenario [GJ/m2]
Energy Use per Unit of Floorspace in Rural Residential Buildings in the Carbon Tax Scenario [GJ/m2]
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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 lighting
! Non-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-using services, such as cooling, appliances, and lighting.
! Price-induced electrification is very small.
0%
10%
20%
30%
40%
50%
60%
70%
0
5
10
15
20
25
30
35
2005 2020 2035 2050 2065 2080 2095 El
ectr
icity
Sha
re in
Tot
al B
uild
ing
Ener
gy
[%]
Baseline[EJ]
Carbon Tax[EJ]
Baseline[%]
Carbon Tax[%]
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Population Weighted HDD/CDD
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Method of Population Weighted HDD/CDD
Overview of HDD/CDD Scenarios Design Method
Monthly and annual 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 time. They capture both extremity and duration of outdoor temperatures.
! Heating degree day (HDD) and cooling degree day (CDD) are measured in “degree-days” below (HDD) or above (CDD) the set point.
! 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:
For Future HDD/CDD
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Scenarios design
Ref*
~ 550 ppmv*
Emissions scenarios
CCSM3
GISS
Hadley
Climate models
Population distribution scenarios
A2r
B1
Fixed
* Reference represented by SRES A2 emissions pathway; 550 ppmv scenario represented by the SRES B1 scenario
HDD &
CDD
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Two Emissions Scenarios
! A2 Emissions (“Reference”) ! High emissions pathway in IPCC SRES scenarios ! Corresponding to Reference Scenario in GCAM
! B1 Emissions (“550ppm”) ! Low emissions pathway in IPCC SRES scenarios ! Corresponding to 550ppm stabilization scenario in GCAM
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CCSM (#15) National Center for
Atmospheric Research, USA (Collins et al., 2006)
GISS (#7) NASA/Goddard Institute for
Space Studies, USA (Russell et al., 2000)
Hadley (#16) Hadley Centre for Climate
Prediction and Research/Met Office, UK (Gordon et al., 2000)
Three Climate Models
Source: Maurer et al. Climate model 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
The three climate models were chosen to represent a range of temperature outcomes.
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Fixed scenario: we maintain the year 2000 population distribution.
Source: A. Grübler et al. / Technological Forecasting & Social Change 74 (2007) 980–1029
Three population distribution scenarios (A2r, B1, Fixed) *Note that this is only
the population distribution. We are using a single population scenario for GCAM analyses
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Step 1:
Annual HDD/CDD Estimation
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HDD/CDD Calculation
Heating Degree Day (HDD)
Cooling Degree Day (CDD)
∑=
−=m
daycMonth THDD
1)18( ∑
=
=12
1MonthMonthy HDDHDD
)18(1
−= ∑=
m
daycMonth TCDD ∑
=
=12
1MonthMonthy CDDCDD
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Monthly mean temperature (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
Ref 550ppm
GISS
Ref 550ppm
Hadley
Ref 550ppm
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-‐30
-‐10
10
30
1 61 121 181 241 301 361
Tempe
rature
Day
Original
Observationally-derived baseline data
Source: Globally Downscaled Climate Data at http://www.engr.scu.edu/~emaurer/global_data/
Variance of Daily Temperature (historical daily data 1950-1999)
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Example
-‐10
10
30
50
1 61 121 181 241 301 361
Temperature
Day
Original
FFT Low Pass
FFT High Pass
Standard Deviation / Variance (seasonal trend removed)
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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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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 population – not on totals.
! 3 distribution scenarios ! 0.5 degree spatial resolution
(same as temperature data) Population
(1990-2100) 10-year Step
A2r
B1
Fixed
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IIASA Population Downscaling Method
53
! 4 steps to downscaling 11 regions-> 185 countries->sub-national (urban &
rural)->grid
! Urban and rural population was disaggregated at national level
! Gravity type model was applied on urban population
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Change in Population Distribution (A2r)
180°
180°
180° 150° E
150° E
120° E
120° E
90° E
90° E
60° E
60° E
30° E
30° E
0°
0°
30° W
30° W
60° W
60° W
90° W
90° W
120° W
120° W
150° W
150° W90°
60° N 60° N
30° N 30° N
0° 0°
30° S 30° S
± 0 5,000 10,0002,500
Kilometers
ratio (populaiton 2100/2000)< 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°
180°
180° 150° E
150° E
120° E
120° E
90° E
90° E
60° E
60° E
30° E
30° E
0°
0°
30° W
30° W
60° W
60° W
90° W
90° W
120° W
120° W
150° W
150° W90°
60° N 60° N
30° N 30° N
0° 0°
30° S 30° S
± 0 5,000 10,0002,500
Kilometers
ratio (populaiton 2100/2000)< 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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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
Population Weighted HDD/CDD
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Population Weighted HDD 2000-2100 (CCSM-ref-A2r)
High
Low
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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 scenario
Increased population in “less-cold” areas
800
1500
2200
2900CCSM-‐Ref-‐A2
GISS-‐Ref-‐A2
Hadley-‐Ref-‐A2
CCSM-‐Ref-‐B1
GISS-‐Ref-‐B1
Hadley-‐Ref-‐B1
CCSM-‐Ref-‐Fix
GISS-‐Ref-‐Fix
Hadley-‐Ref-‐Fix 800
1500
2200
2900CCSM-‐550-‐A2
GISS-‐550-‐A2
Hadley-‐550-‐A2
CCSM-‐550-‐B1
GISS-‐550-‐B1
Hadley-‐550-‐B1
CCSM-‐550-‐Fix
GISS-‐550-‐Fix
Hadley-‐550-‐Fix
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2500
3300
4100
4900
5700CCSM-‐550-‐A2
GISS-‐550-‐A2
Hadley-‐550-‐A2
CCSM-‐550-‐B1
GISS-‐550-‐B1
Hadley-‐550-‐B1
CCSM-‐550-‐Fix
GISS-‐550-‐Fix
Hadley-‐550-‐Fix2500
3300
4100
4900
5700CCSM-‐Ref-‐A2
GISS-‐Ref-‐A2
Hadley-‐Ref-‐A2
CCSM-‐Ref-‐B1
GISS-‐Ref-‐B1
Hadley-‐Ref-‐B1
CCSM-‐Ref-‐Fix
GISS-‐Ref-‐Fix
Hadley-‐Ref-‐Fix
Global Population Weighted CDD
Ref emissions scenario 550 ppm emissions scenario
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Regional HDD/CDD
Global map of GCAM regions
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Emission Scenarios (Ref & 550ppm) based on CCSM & A2r pop
2000
3000
4000
5000
USA_CCSM-‐ref-‐A2r
USA_CCSM-‐550-‐A2r
2000
3000
4000
5000
China_CCSM-‐ref-‐A2r
China_CCSM-‐550-‐A2r
1000
2000
3000
4000
China_CCSM-‐ref-‐A2r
China_CCSM-‐550-‐A2r
1000
2000
3000
4000
USA_CCSM-‐ref-‐A2r
USA_CCSM-‐550-‐A2r
USA HDD China HDD
USA CDD China CDD
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Climate Models (CCSM, GISS, & Hadley) based on ref and A2r pop
2000
3000
4000
5000
USA_CCSM-‐ref-‐A2r
USA_GISS-‐ref-‐A2r
USA_HADLEY-‐ref-‐A2r
2000
3000
4000
5000
China_CCSM-‐ref-‐A2r
China_GISS-‐ref-‐A2r
China_HADLEY-‐ref-‐A2r
0
1000
2000
3000
4000
USA_CCSM-‐ref-‐A2r
USA_GISS-‐ref-‐A2r
USA_HADLEY-‐ref-‐A2r0
1000
2000
3000
4000
China_CCSM-‐ref-‐A2r
China_GISS-‐ref-‐A2r
China_HADLEY-‐ref-‐A2r
USA HDD China HDD
USA CDD China CDD
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Population Scenarios (A2r, B1 & Fixed) based on CCSM and ref
1000
2000
3000
4000
China_CCSM-‐ref-‐A2r
China_CCSM-‐ref-‐B1
China_CCSM-‐ref-‐Fix
1000
2000
3000
4000
USA_CCSM-‐ref-‐A2r
USA_CCSM-‐ref-‐B1
USA_CCSM-‐ref-‐Fix
2000
3000
4000
5000
China_CCSM-‐ref-‐A2r
China_CCSM-‐ref-‐B1
China_CCSM-‐ref-‐Fix
2000
3000
4000
5000
USA_CCSM-‐ref-‐A2r
USA_CCSM-‐ref-‐B1
USA_CCSM-‐ref-‐Fix
USA HDD China HDD
USA CDD China CDD
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Summary of population weighted HDD/CDD
Emissions: Emission scenarios have important impact on population weighted HDD/CDD, and the difference between 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 different impact on population weighted HDD/CDD in different level and in different regions. At global level, population 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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Implementation of Emissions Scenarios in GCAM
! Reference scenario: SRES A2 emissions path • No climate mitigation policy, business-as-usual
! 550ppm stabilization scenario: SRES B1 emissions path • 3 emissions trading markets (China, U.S., and ROW) ensuring 550ppm
global emissions path. • China emissions pathways do not vary across climate and migration
scenarios to enable comparison of energy consumption in the buildings sector.
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US Building Final Energy Use
! In the reference scenario, climate change lowers US buildings’ final energy by 6% or less.
! Stabilization policy reduces the shifts in 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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Hea-ng/Cooling Energy Use in China (cumula;ve energy in EJ, 2005-‐2095)
! Less heating energy use & more cooling energy use with climate change ! Migration decreases heating energy use but increases cooling energy use: Urban growth and its spillover concentrated in ‘less-cold’ eastern areas
Comparison across migration scenarios Comparison across climate models
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US Building Energy Use by Fuel
* The left figure shows ‘Ref-Fix’ case and the right figure shows ‘CCSM-Ref-Fix’ case.
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China Building Energy Use by Fuel
* The left figure shows ‘Ref-Fix’ case and the right figure shows ‘CCSM-Ref-Fix’ case.
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! 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 are distributed across various fossil fuels
Changes in Building Final Energy Use in China (cumula;ve energy in EJ, 2005-‐2095)
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! Less use of direct fossil fuels (gas, oil, and biomass) and more use of electricity
! Regardless of emissions scenarios,
reduction in gas consumption accounts for the majority of the decrease in building energy use.
Changes in Building Final Energy Use in the U.S. (cumula;ve energy in EJ, 2005-‐2095)
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CO2 Emissions from the Buildings Sector
! Climate change will decarbonize the buildings sector, and the effect is more pronounced in China than in the U.S.
! In terms of total emissions
(including power sector emissions), global climate change virtually has no effect on the sector’s emissions.
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Assessing the Long-term Impact of China Building Codes
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Downscaling the China Buildings Sector
Cold
Severe Cold
Hot Summer Cold Winter
Hot Summer Warm Winter
Temperate
Severe Cold
Cold
Cold
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79
China
Cold
Urban Residen;al
Rural Residen;al
Commercial
Hot Summer Cold Winter
Urban Residen;al
Rural Residen;al
Commercial
Hot Summer Warm Winter
Urban Residen;al
Rural Residen;al
Commercial
Severe Cold
Urban Residen;al
Rural Residen;al
Commercial
9 Provinces Beijing, Tianjin, Hebei, Shanxi, Shandong, Henan, Tibet, Shaanxi, Gansu
9 Provinces Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan
6 Provinces & 2 countries Fujian, Guangdong, Guangxi, Hainan, Guizhou, Yunnan Cambodia, Vietnam
6 Provinces & 2 countries Inner Mongolia, Jilin, Heilongjiang, Qinghai, Xinjiang, Liaoning, Mongolia, DPRK
The Twelve Buildings Sectors
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Global Change Assessment
Model (GCAM) Building Stock Model
Climate Change Impact: Hea;ng/Cooling Degree Days by Sub-‐Region
Detailed Building
Energy Model
Historical info: Construc;on Building types Codes/standards U value
Base-‐year energy consump;on by fuel and service
Building Energy Results
Assump;ons about future building
codes, compliance, and building retrofit
Modeling Overview
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Building Energy Consumption: The Three Building 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
! Current ! Development of a Global Building Model ! Multi-model Detailed assessment of US Building Sector:
Linking to BEND and WRF in Richland
! Future ! Development of a population migration model linked to income
distribution and climate condition ! Climate feedback and downscaling