20th AIM International Workshop, poster no. 17 presentation summary

Impacts of low-carbon technologies for the Chinese residential sector at provincial-level
Rui Xing,* Tatsuya Hanaoka, Yuko Kanamori, Hancheng Dai, Toshihiko Masui
National Institute for Environmental Studies, Japan*Contact: xing.rui@nies.go.jp
0.7

1. Introduction
Until recently rural areas accounted for a majority ofpopulation in China. In 2011 urban population surpassed rural
population for the first time due to urbanization process, still
there are over 650 million people and over 210 million
households [1] live in rural areas. Instead of commercial energy,
most of the rural households rely on straw and firewood fortheir daily energy use. It is necessary to increase rural access toless polluting commercial energy and alleviate environmental
problems caused by the highly unsustainable energy structure.

Satisfaction factor

0.6
0.5
0.4

0.3
0.2
0.1

0.0
0500

1000

1500

2000

2500

Per capita income (2011 US$)

Service demand (ktoe)

2. Estimation of service demands
Figure 1. Satisfaction factors of hot water use
In this research, the service demands of rural residential
sector are estimated by a macro-model [2] through 2030. The
Heating
Hot Awater
Cooking
BC A DE CABCD A EBCDE ABC
DE BLighting
D AOthers
E BCDE
macro-model simulates the demands for final energy services
BeijingShanghaion the parameter inputs of futures trends in a variety of factors.
Beijing
Beijing
BeijingShanghai
BeijingShanghai
BeijingShanghai
Shanghai
Shanghai
400400 400
400400The urbanization process is also taken into
account400 byestimating future rural population and floor area. The model
estimates the changes in service demands
different
300 by300
300 300
300
300
applications every 5 years.
The preliminary estimation from the model
200 appears to200 be
200 200
200
200much higher than statistical data [1] in base year (2010). One
possible reason is that in the model energy usage
patterns such
100
100
100 100
100
100
like frequency of shower times and operation hours ofhousehold electronics are set on standard level0 of developed
00000
countries. While in rural China these usage terms may
not be2010
2020
2030
2010
2010
2020 2010
2030 2020 2010 2010
2030 2020 20202010 2010
2030 2030
2020 20202010 201020302030
2020 2020 202020102030 2030 2030
2020 2010
2030 2020
fully applied due to the limited financial resource. Thus we
Figure 2. Estimation of rural residential service demands
introduce corrective coefficients – satisfaction factors tocalibrate the model estimation. The calibrated service demandbottom-up optimization model with a detailed mitigation
is calculated by using Equation (1).
technology database, to estimate mitigation potential of
low-carbon policies. In the base year (2010) the estimated(1)
𝑆𝐷𝑖 = 𝑈𝑖 × 𝐴𝑖 × 𝑆𝐹𝑖
service demands are disaggregated to energy usage of existing
Here, SDi is service demand of each province. Ui represents
technologies, while in the target year (2030) service demands
are satisfied with energy efficient technologies. The model
service demand of each application per household or per floor
selects technology combination for each province under thearea, Ai represents amount of household or floor area, SFi
represents satisfaction factors.
least total-system cost.
Satisfaction factors are coefficients which evaluateadequacies of service demands and are closely related to per4. Results and discussions
Figure 2 indicates breaks downs of service demands fromcapita income. Satisfaction factor of each application is2 regions as an example. In most of the provinces rural
resulted by optimization analysis in order to keep model
estimation consistent with historical data in the base year.
residential service demands will decrease in the future due tourbanization process. Still implementation of low-carbon
For hot water application, SF is calculated by Equation
technologies will improve rural energy structure and bring CO2(2).
emission reductions as well as financial benefits.
18653  PCICi
SFi (2)17034  ( PCICi  1619)
Acknowledgments
This research was supported by the Environment Researchwhere PCICi is rural per capita income (2011 US$) of eachand Technology Development Fund (S-12-2) of the Ministry ofprovince.
the Environment, Japan.
For other applications SF is calculated by Equation (3).
SFi 25775  PCICi
17034  ( PCICi  8741)(3)

Figure 1 shows satisfaction factors of hot water use and per
capita incomes of 31 provinces.
3. Low-carbon technologies
To select effective low-carbon technologies for ruralresidential sector, we use the AIM/Enduse model [3], aReferences[1] China Statistical Yearbook.
http://www.stats.gov.cn/tjsj/ndsj/ (Accessed 03.09.2014).
[2] Xing R. and Ikaga T., Assessment of low-carbon policy
implications in China’s urban residential sector,
International Journal of Sustainable Building Technology
and Urban Development 4(3): 229-242, 2013.
[3] Kainuma M. (ed.), et al. Climate Policy Assessment:
Asia-Pacific Integrated Modeling. Tokyo: Springer, 2003.

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