linking clm and ipets to climate impacts on energy use · government, exports/ imports final goods...

Linking CLM and iPETSto estimate climate impacts

on building energy use

Bas van Ruijven, Keith OlesonNCAR – CGD – IAM

Glacier

Lake

River Routing

Runoff

River discharge

Urban

Land Use Change

Wood harvest

Disturbance

Vegetation Dynamics

Growth

CompetitionWetland

CLM

CLM as Physical Impacts Model:Part of the integrated assessment framework!

CESM

iPETS

2Climate impacts on building energy use

Glacier

Lake

River Routing

Runoff

River discharge

Urban

Land Use Change

Wood harvest

Disturbance

Vegetation Dynamics

Growth

CompetitionWetland

CLM


Crop/forest yieldBuilding energy useUrban heat wavesLU emissionsWater availabilityRenewablespotentialsCESM

iPETS


Glacier

Lake

River Routing

Runoff

River discharge

Urban

Land Use Change

Wood harvest

Disturbance

Vegetation Dynamics

Growth

CompetitionWetland

CLM


Agricultural land useLand management

Wood harvestUrbanization

Crop/forest yieldBuilding energy useUrban heat wavesLU emissionsWater availabilityRenewablespotentialsCESM

iPETS


This study

• Objective:– analyze the impacts of climate change on household energy use

• Scenarios:– CLM: CESM RCP 8.5 temperature/humidity/radiation– iPETS: SSP5 socioeconomic scenario,

• low population growth• high economic development• high per capita energy use and emissions

Climate impacts on building energy use 3

Change in… CESMCLMU

Economic IAM (iPETS)

… temperature

… demand for cooling/heating service

… energy use for cooling/heating

… energy use for buildings/HHs

… energy system

… CO2 emissions

… energy expenditures

… household expenditures

… macro‐economy (GDP)

Horses for courses



Physical IAM (IMAGE)


… temperature




… energy system

… CO2 emissions




Horses for courses


Share of heating/cooling in HH energy use


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 2020 2040 2060 2080 2100

Share cooling in ELC use (from IMAGE, no climate) CHN

EU27

IND

LAC

ODC

OIC

SSA

TC

USA0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 2020 2040 2060 2080 2100

Share heating in fuel use (from IMAGE, no climate) CHN

EU27

IND

LAC

ODC

OIC

SSA

TC

USA

ConductionConvectionRadiationVentilation

•The model determines energy demand through • Building thermal properties (heat transfer through roofs/walls/floors)• Wasteheat factors (efficiency of heating/cooling systems and the

conversion of primary to end use energy)• Building thermostat settings

CLM Building Energy Model

K.W. Oleson, G.B. Bonan, J. Feddema and T. Jackson, An examination of urban heat island characteristics in a global climate model, Int. J. Climatol. 31: 1848–1865 (2011)

Change in heating/cooling energy use in CLM, RCP8.5


0

0.2

0.4

0.6

0.8

1

1.2

2000 2020 2040 2060 2080 2100

2004

=1

Heating

0

5

10

15

20

25

2000 2020 2040 2060 2080 210020

04=1

Cooling

China EU27 India Latin America Other Developing C's

Other Industrialized C's sub‐Saharan Africa Transition Countries USA

Population‐Economy‐Technology Model

iPETS Model

8O'Neill BC, Dalton M, Fuchs R, Jiang L, Pachauri S, Zigova K. Global demographic trends and future carbon emissions. Proceedings of the National Academy of Sciences 2010;107; 17521‐17526.


iPETS Model

Consumption, Investment,Government, Exports/

Imports

Final Goods Producers

Consumption & SavingsCapital & Labor

Households

Oil, Gas, Coal, ElectricityRefined Fuels, Agriculture,

Materials

Intermediate Goods Producers


LaborCapital

FinalGoods

Energy

Materials


iPETS Model


Imports



Households


Materials



LaborCapital

FinalGoods

Energy

Materials


iPETS Model

CO2 Emissions


Imports



Households


Materials



LaborCapital

FinalGoods

Energy

Materials


iPETS Model

CO2 Emissions


Imports



Households


Materials


Land Use


Heating/Cooling impacts in iPETS

• No explicit modeling of heating/cooling– Six goods for consumption– Electricity, CoalBio, Other energy, Food, Transport, Other

• Utility consumption preference, price, income• Simulate decisions under changing circumstances

• More cooling ‐> increase in electricity preference• Less heating ‐> decrease in “other energy” preference• Results influenced by preference change and prices


Change in household final energy use in iPETS


0

20

40

60

80

100

120

140

160

180

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

CHN EU IND LAC ODC OIC SSA TC USA

GJ per ca

pita

2100

0

10

20

30

40

50

60

70

80

90

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

Base

RCP8

.5

CHN EU IND LAC ODC OIC SSA TC USA

GJ per ca

pita

2050

Coal Biomass Liquids Gases Electricity

Wrapping upFindings• Climate impacts on cooling energy use are large in CLM for

several regions. – This can be due to thermostat settings, time resolution,

buildings/walls• Temperature change under RCP8.5 leads to considerable

changes in household energy use.

Next steps• Explore the large cooling impacts by doing an in‐depth

comparison between CLMU and IMAGE (physical IAM)• Provide socioeconomic changes from IAM framework to CLM

(such as change in urban area, income, AC‐ownership)


Model differencesCESMCLMU

Physical IAM (IMAGE)


Main model unit Watt EJ/yr $

Time resolution 30 min annual annual

Climate information Temperature Degreedays ‐

Set point Min: 13‐20 oCMax: 28‐38 oC

18 oC ‐

Scope Urban building energy

Energy, emissions Economy, energy, emissions

Socioeconomic changes

‐ Population, GDP, technology

Population, GDP, technology

Nr. world regions 33 26 9

Sub‐categories TBD/HD/MD Urban/rural,income

‐


IMAGE ‐ heating

13

Heating Floorspace HDD Intensity

0

10

20

30

40

50

60

70

0 10000 20000 30000 40000 50000 60000 70000

USD2000/capita/yr

m2 /c

apita

UN Habitat Shen, 2006 Rural India Urban India IEA 2004 Eurostat Urban Audit

oCm2 kJ/m2/oC

0

50

100

150

200

Cana

daUSA

Mexico

Rest C. A

merica

Brazil

Rest S. A

merica

N. A

frica

W. A

frica

E. Africa

South Afric

aW. Europ

eC. Europ

eTu

rkey

Ukraine

Stan

Asia

Russia

Middle East

India

Korea

China

SE. A

siaIndo

nesia

Japan

Oceania

Rest S. A

siaRe

st S. A

frica

kJ/m

2 /de

gree

2007 2100

Floor space Intensity

van Ruijven BJ, van Vuuren DP, de Vries HJM, Isaac M, van der Sluijs JP, Lucas PL, Balachandra P. Model projections for household energy use in India. Energy Policy 2011;39; 7747‐7761.

IMAGE ‐ cooling

14

UECefficiencyCooling HHs penetration

penetration ClimateMaxSaturation×availability

Isaac M, van Vuuren D, P. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 2009;37; 507‐521.

( )UEC CDD× f income



… temperature




… energy system

… CO2 emissions




Horses for courses


linking clm and ipets to climate impacts on energy use · government, exports/ imports final goods...

Documents