Effects of climate changes on
Energy Consumption in China
Principals ：Ren Yuyu1, Qian Huaisui2, Sun Jiulin3, Li Zehui3
Units：
1. Institute of Environment and Planning, Henan University
2. Institute of geography science, Guangzhou university
3. Institute of Geographical Sciences and Natural
Resources Research, CAS

1 Background¾

Energy is one of the key problem over the world¾ The research on relationship of climate changes andenergy consumption become a hot spots .
The scholars pay more attention to¾

How the energy consumption effect climatechanges

Pay less attention to¾ The anti-influence of climate changes on energy consumption¾ With the unceasing enhancement of Chinese economy andpeople’s living standards, weather-sensitive component play
an more and more important role in the total load.
¾ Studying the effects of climate changes on energy
consumption can provide scientific basis for the formulation ofenergy plan and strategy, as well as the energy expend forecast.

2 data and method
2.1 data and origin¾ The decade-day temperature data were provided byIGSNRR,CAS. and China MeteorologicalAdministration.
¾ Energy consumption data were got from Statistical
yearbook¾ Society economical data were provided by
IGSNRR,CAS¾ The future scenario data were downloaded fromhttp://www-iam.nies.go.jp/impact/country/.

2.2 The degree-day calculation method
2.2.1 the selection of calculation method:
Comparing the correlation coefficient and themulti-annual mean error between the actual value
and the result of following calculation methods,
we select the decade-day temperature direct
calculation method.
¾ Wayne’s fast monthly degree-day
calculation
method¾ The interpolating method and smoothing methodof decade mean temperature which set up by Long
Siyu
¾ Decade-day temperature direct calculation method

2.2 The degree-day
calculation method
2.2.2 Steps¾ Calculate the year degree-day of the 29 capitals from 1970-1999,¾ We found decade-day temperature direct calculation
method has the higher precision and is simpler than theothers.
The computation expressions :z Xm=（Td－Tb）×m
Where Xm is the sum of degree-day，Td is the mean of decade-day
temperature, Tb is the basic temperature, m is the number of the decade-daydays. At last sum the results decade-day directly and get the total year degreedays.

2.3 the climate-load abstraction method
The energy consumption is affected by societal economical factors andclimatic factors, as they have different change character , we can present theenergy consumption by the following expression:z y=yt+yw+yc
y is the actual energy consumption, yt is the tendency energy consumption,yw is
the fluctuation consumption, yc is the random variable.

¾.

The tendency consumption (yt) change is gradual and presented as theactive function of time. We can use multinomial method to model society
economy consumption.The climate consumption (yw)is the part affected byclimatic factors primarily the natural disaster and temperature. Randomvariable(yc) occupies a little part in the total and is difficult to distill that isalways been omitted. So we can use the difference between actual andsociety consumption to present the climate consumption

2.4 The future climate scenarios¾

Forecast date source
Adopt the interpolated GCM outputs (0.5o x 0.5o) 2020-2029 and2050-2059 climate forecast date of Canadian Center for Climate
Modeling and Analysis projected by CCCma and CCNI model withassuming the A2 GHGs emission scenario in IS92 edition published byIPCC.

¾ Material steps of data correct
Compute the difference forecast date between 1990-1999, 2020-2029,
2050-2059 in different area individually.
Use the change value predicted by Chinese scholar for reference andget the revised change value in the areas,
Plus the actual mean monthly temperature in 1990-1999, we concern
the result as the mean monthly temperature in 2020-2029 and 20502059.(Tab1)

Tab.1 The difference of mean degree-day between 1990－1999 and the futureclimate scenarios

2.5 forecast model¾Expressions

L(t) = N (t) + L(w),L(w) = N(t)W(T) ,L(t) = N (t) + N(t)W(T) = N (t)(1+W(T))
For computational convenience, this can be rewritten, with W = 1+W(T),thenL(t) = N(t)W,
W = L(t) ÷ N(t)Where L(t) is the total load, N(t) is the tendency consumption, L(w) is the climate consumption, W(T)is the weather sensitive coefficient.
¾ The calculation of W

This paper use following models respectively to simulate W :multinomial： y= b0+ax +bx2 +cx3
liner：y=b0+ax
exponent：y=b0 (ax)Growth：y=e (b0 +a/x)
Exponential：y=b0*e (ax)Where y is weather sensitive coefficient W, x is the year total degree-day, A，b0，b and c arecoefficient.

Investigating those model, we discover that to different province there isdifferent suitable model. Using different models we get the W of 2020-2029,2050-2059.(Tab 2)

Tab 2. the future W of per-province.

3 The future climate consumption
scenario
Basing on the U.S.A.'s energy-consuming situation,
we can approximately predict the volume of energy
consumption in 2020-2029 in China and then deduce
the energy consumption situation of every province.
Using MapInfo software to classify the provinces
according to standard deviations of their climate
consumption situations and the changes comparedwith 1990-1999.
The results are presented in chart 1 and chart 2.

3.1The climate consumption scenario analyse (fig 1)

From the fig 1, we can say that:¾ In 2020-2029, if in the first scenario, Anhui, Hebei and Hunan have thelargest climate consumption in China, while Shandong and Sichuan isthe lowest and Jiangsu, Henan, Shanxi Guangdong and Guizhou,Guangxi in southwest are next to them. If in the second scenario, thenHebei, Shanxi and Hunan belong to the highest climate consumption
rank, and the next rank include Mongolia, Shandong, Anhui, Jiangxi andYunnan, while Sichuan Province is the other extreme, and Guangxi andHenan are next to it.
¾ In the forecast of 2050-2059 climate consumption scenario, Jiangxi
Province has the largest climate consumption forecast, Mongolia andHebei Province are next to it. Sichuan and Hunan, Guangxi have thelargest positive change, Xinjiang, Qinghai, Ningxia, Yunnan, Guizhou,Henan, Hubei, Jiangxi and the coastal province in east China belong to
the fourth echelon3.2 The analyse of climate consumption change (fig 2)

From the chart2, we can say that:
Comparing with 1990 - 1999 situation:¾ In 2020 –2029 climate consumption scenario, if in the firstscenario, Anhui, Hebei and Hunan have the largest enhancement
range in China, the next is Heilongjiang, Qinghai, Shanxi, Hubei,Jiangxi as well as coastal provinces in eastern China, Sichuan andShandong have the largest drop range. If the second scenario, theprovinces belonging to the first rank which have the largest active
change scope are Hebei, Shanxi, Hunan, the second rank include
Mongolia, Shandong, Jiangsu, Anhui and Jiangxi, at the sametime Sichuan has the largest drop change, and the next to Sichuan
is Guangxi.
¾ In 2050-2059 climate consumption scenario, Mongolia andShanxi and Hebei belong to the highest enhancement rank, thethree provinces in Northeast China, Tianjin, Shandong, Anhui,Gansu and Shanxi are the next rank, the last rank which have thelargest drop scope contains only Sichuan Province, change scopesof the rest are immediacy.

We can see that the distribution and change situation ofChina future climate consumption has put up some regional
characters:¾ The enhanced provinces mainly include those lying in theeastern coastal area and the area close to them in the 20202029(1) scenario. When come to the second one, thedistribution of enhanced provinces is more disperse thanthat in the first, but there is still regional character and thedistribution areas are the 115-120oN area north to theChangjiang River, Mongolia and the Xianggan area in theJiangnan region.
¾ Under 2050-2059 climate scenario, the majority of Chinese
north provinces, compared with 1990-1999, have active
change in climate consumption, while in the south provinces
the scene are just upset, their future energy consumption
scenarios drop by varying degree. We consider climatefactor as one of the prime influence factors, and the analyzeof the degree-day also verify that conclusion.

3.3 The analyse of climate consumption change¾

Climate factor is one of the prime influence factors of energy
consumption
In 2020-2029 (1) climate scene, the climate factors of Anhuiand Hunan Provinces increase obviously and the factor of Sichuan
Province descend markedly, which make the difference of changesituations between the three provinces very clear. In 2050-2059
climate scenario, the drops of climate factor in Sichuan andGuangxi Province, compared with 1990-1999, is very significant
so is the climate consumption scenario in the two provinces.

¾Although change of the climate factor can explain the change ofclimate consumption partly, we still should pay much attention to
the influence of economy factor. So when analyzing the course ofenergy consumption change , we must discuss the two aspects
synthetically.

4 Results and discussion
4.1Results¾¾¾

Get the mean monthly forecast temperature in 2020-2029 and 20502059.
Get the distribution of Chinese climate consumption in future climate
scenarios.
Get changes of Chinese climate consumption between the three futureclimate scenarios.

4.2Discussion
Since the lack of energy consumption data, the immaturity of thesimulation model and the complexity of the relationship between thetwo drivers and the climate consumption, there are some defects inthis paper to further research.

4.3Annotation
Because of the shortage of energy date in Tibet, Hong Kong,
Macao and Taiwan area this article does not involve these areas
temporarily.

The endThanks