Assessment of Low-Carbon Technologies for theChinese Urban Residential Sector
Introduction

Rui, XINGPart 3. Technology options for efficiency scenarios

The economy of China has developed rapidly• Each existing technology is grouped with one NEW technology
and one BAT (best available) technology• AIM-Enduse model gives the best technology combination ofcost-benefit performance based on optimization analysis

Demand for higher living standards and energy consumption
System Analysis

Technology list (partial):・ Bottom-up modelization of household energy system

Initial Investment Cost・ Energy efficiency scenarios through 2050Payback Time

Thermal retrofitting

30,100 USD per toe

30 yearsTechnology evaluations on energy efficiency of Chinese urbanhouseholds

High-efficiency
air-conditioner

17,300 USD per toe

15 years

Methods

High-efficiency
Water heater (NG)16,400 USD per toe

15 years

Part 1. Modelization of household energy system

Electric range

32,000 USD per toe

15 years

LED

1,100 USD per toe

10 years

Energy Service

High-efficiency
refrigerator

39,800 USD per toe

10 years

Warming & cooling
Domestic hot waterLighting
Cooking
Household electronics
etc.

Results

Bottom-up Analysis Flowchart•••••EnergyEnergy Technology

CoalOilGas
Renewables
Electricityetc.

• District heating• Air conditioner• Fluorescent• Electronics• Water heateretc.

••
•
•
•

CO2 emission prediction of 9 eastern regions
25.0• Rated power• Operating period• Heating / coolingload• Lifestyle
etc.

••
•

Household number
Household size
Floor areaetc.

CO2 emission (MtCO2eq)

20.0

15.0

10.0

5.0

Part 2. Model calibration

0.0"00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30 "00 "20 "30• Model estimates constantly appear to be larger than statistics• In China the demand has not been fully satisfied due tolimited financial resources• “Satisfaction factor” is introduced to relate energy
consumption pattern to economic level(GDPPCi < 17,034intl.$*)SFi  100 %(GDPPCi ≥ 17,034intl.$)

Satisfaction factor of heating

Statistics and model estimates (2005)

1.0

8000

Gap between stat. and estimatesstill needs to be improved

0.9
0.7
Each dot represent a division

0.6
0.5
0.4
0.3
0.2Macro data (ktoe)

Satisfaction factor

0.8

6000

2000

4000

6000

8000

GDP per capita (USD)

10000

CHQHUNHELBEJSHH

4000

6000

Model estimates (ktoe)

JIX

GUD

0

High-efficiency air-conditioner(cooling)

-100

LED

-200

High-efficiency air-conditioner(warming)

High-efficiency appliances-300

Solar water heater

-400
0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Reduction quantity (MtCO2eq)(HEL=Heilongjiang, BEI=Beijing, SHH=Shanghai, GUD=Guangdong; results of totally 31 regions are available)

・ The efficiency technologies bring CO2 emission reduction incountermeasure scenario

・ More accurate service demand prediction
2000

HUB

Marginal abatement cost curve (countermeasure scenario, 2030)

2000

0ZHJ

CM

Further Work

4000

00JIS・ Emission reduction could be achieved with gain of financial
profits (negative abatement cost)

0.1
0.0

HEN

(BL=baseline, CM=countermeasure; results of totally 31 regions are available)

0.0

GDPPCi is GDP per capita (PPP, intl. $, 2000), a is a constant resulted from optimization analysis*17,034intl.$: The lowest GDP per capita (Turkey) in OECD countries (stats.oecd.org)

ANHBL

Abatement cost (US$/tCO2eq)

Satisfaction Factor
GDPPCiSFi GDPPCi  aSHH

8000

・ Rural residential area, other sectors

4.5