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1 = = = 8 = 4!"#$% PROGRESSUS INQUISITIONES DE MUTATIONE CLIMATIS Vol. 8 No. 4 July 2012 doi: /j.issn ,,,.!"#$%&'()"*+,=[j].!"#$%, 2012, 8 (4): 278J284!"#$%&'!"# == N == N N NIO N=!"#!$%=NMMMUQ O=!"#=NMMMQR =!"#$%&'()* 2020!"#$%&'(!"#$%&'(!"#$%&'!" 30!" 6!"#$!"#$ 12!"#$%&' 55% 65%!"#$%&'!"#$!"#$%35!"#$%&' 48% 61%!"#!!"#$%&'()*+,- 5!!"#$%&'()*23!"#$%&'( 54%49% 49% 3!"#$!"!"#$%&'()* 4 6!"#!"#40%!"#$%!&'()*&+,-./012!"#$%&'()*+,-./01!"#!"#$%&'()*+,-./!!"#$%!"! = ====2009!"#$%&'()*+,!"#$% 2020!"#$%& GDPCO 2!"#$%& %45%!"!"#$%&'!!"#$%&'()*+,-./!"#$%&'()*+, ==== x1j2z!"#$%&'(!!"#$%&!"#$%&'()*!"#!"#$%& '()!"#$%&'()*+,-./0!"#$%&!!"!"!"#$%& x3z!"#$%&'( x4z!"#$%&!"#$!" 31!" 5!" 10% 18% 5!"#$%&'(!")!"#$%!"#$%&7 10%20% 7!"#$!!"#$%&!"#$%&!"#$%!"#$%&'()*+!"#$%&' ====!"#$%&'()*+&',-.!"#$%&'!"!"#$! ; =! !!"#$%2009BAC62B01!!!"#!"#$%&'(!"#$!caiwj05@mails.tsinghua.edu.cn

2 4 =!"#$%&'()"*+, 279!"#$! "#$%&'()*+!"#$%&'()*!"#$%&!"#$%&'()!"#$%&'!"#! x1j2z!"#$!"#$%&'() N==!"#$ NKN==!"#$%& ====!"#$%&'!"#$%&'!"#!"#$ x1j2z! 7!"#$%&'(3!"#1!"#$ GDP!"#$%&!"#$%&'(!"!"#!"#$%&'$()*!"#$%!"#$%&'(&')*+,-./01!"#$%&!!!"#$!"#$%&!"#!!!"#$%!"#$!"#$%&'()*+'!"#$%&'()*+,-./-0'!"!"#$! GDP!!"#!$%&'()*(+,-#!./!"#$%!"#$%&'()*+!"#$%!"#$!"#$!"#!"#$%&'(%&)*+!"#$%!"#$%&'()*+ 1=! Table 1 Indicators for cluster analysis!! GDP!"!!"!"! GDP!"#$"%&!"#$%&!!"#$%!"#!!"#$%&!"#!"#$!"#$%&'!"#$%&'()!"!"!"#$%&!'(!"#!$%&'!"#$%&'(!"#$!"#$%&'()*+,!"#$%&'()!"#$!"#$%&'()*+,-.!"!"#$%&!"#$%&'()*!"#$%&'()*+,- ====!"#$!"#$%&'()$!!"#$%&'(!"#$!"!"#$!"%! x5j7z!!"#$%&!!"#$%&' ()!"#$%&!"#!"!"#$!"#$%&' ====!"#$%&'()*+,- x8z!"#$%&!"!"#!"#$%&' x5z!"#$!"#$%&'()!*+,&- x 6J7z!"#$%&'()*+,-.!!"#$!"#$%&' S!"!"n S k k!! n k S k = Σ (X i J X k)(x i J X k) (1) i=1 n k k!"#$%x i i 7!"#$%&X k k!"!"#$%s = Σ n S j=1 j!"#$%&!"#$%&!"#$%&' S!"#$%& ====!"#$%&!"#$%&'(!"#$%&'()*!"#$% K!"#$%!"1) k!"2)!"#$%&'()*+,-!"#$%&'()$%&'*+,-!"#$%!"#$%! !"#$%&'()*+*,

3 = = =!"#$% 280 = 2012!"#$%&3)!2)!"#$! NKO==!"#$%& ==== G i i!"#$%&g i! G i = a W i ============================(2)!a!"a[0W i 0YW i Y1 i!"#$%&!"#$%&'(!"#$!%&'()*+ Y i,20 I 20 = Σ=====I i,20 =======================(3) i =1 Y 20 I i,20 I 20!i!"# 2020 Y i,20 Y 20! i!"# 2020 GDP ====(3) 2020!"#$%&'()*+ 2005!"I 05!"#$%&!"#$%&'()*+,- 45% I 05 (1J45%) = Σ=====I i,05 (1Ja W i )= (4) i=1 Y i,20 Y 20!"#$%&'()*+, GDP!"#$ a! (2)!"#$%& G i ====W i!"#$%&'()*+,-.!"# 7 W i = Σ=α j == j =1 x i,j x j,max =======================(5) x j,max j=1i2ii7 1 j!!"#$%&x i, j=1i2ii7 j i j!"#$%&α j!"#$%& Σ 7 j =1α j =1 α j!"#$%&'()*+ NKP==! ====!"#$%&'()*+,-./ x9z!"#$%&'()*!"#$%&!"#$%&'()!*+,-.!!"#!"#$%&'()$*+ x1z!"#$%&'()*+,-.!"#!"#$%&'()*+"#! x2z ====!"#$%&!"#$%&'(!"#$%&!"#$%&'()*!!"#!"#$%&'()!"#$% 4!!"#$%& 1/4!"#!"#$%&'()*+ 3!!"#$%&1/3!"# 1 4!"#$% 1/8 1/2 3!"#$% 1/6! 1/2!"!"#$%&'(!"#$%&'(!"#$%&'()*+$,-./!!"#$%&'()*+,*$-!"#$%&'()*+,!" x1i10z!"#$%&!"#!"#$%&'()*!"#$%&!"#$%&'()*+,-./012!"#$!"#$%&'()*+,!"#$%&'()!&*+,-.!!"#$%&'()*+,-./ NKQ==!"#$ ====!"#$2005!"!" 2005!"#$%&'() CO 2!!"#$%&'()*!IPCC x11z!"#$%& x12z!"#$!"#$%2005!"#$%&!"#$ CO 2 44 E CO 2 = Σ A i e i c i o i == ==========(6) i 12 A i i!t m 3 e i i!"# kj/m 3 c i i! kg C/GJo i i!"#$!"#$%16!"2! e i!"#$%& x12z!"#!"#$ipcc x11z!"#$%&'(!12!"a i!"#$% A i = F i + P i + H i J=N i ====================(7)

4 4 =!"#$%&'()"*+, 281 F i i!"#$p i H i!" i!"#$%&'()* N i i!"#$%& 1 2=!"#$%& Table 2 Coefficients of various fossil fuels!!"# e i!" c i /(kg C/GJ)! CO 2! E4.3 t CO 2 / F !"#$ Fig. 1 Provincial CO 2 intensity in 2005! ====1!"#$%&'()*+GDP! kj/m !"#$%GDP!"#"$%&'( !"#$%&' !" !"#$%&'()*+,-./ !"#$%&'(2020 GDP !"!"!"#$%& ! !"#$% 3!"! O==!" kj/m !"# !"# ====!"#$!"#$%&'(!"#$%&30!!"#$% 2005!"3.19 t CO 2 /GDP2005!" 45%!"#2020!" 1.75 t CO 2 / 2005!"#$%& OKN==! ====!"#$%&'(! 7!!"#$!"#$%!!"#$%&' 2!"# 4 6 4!"#$!17!!"#$%&'()*!"#$%&'()5!"#6!6!"#$%&'(K 3= GDP!"# Table 3 Projections of annual average growth rate of provincial GDP GDP /% GDP /% =!"#!" CO 2!"#$%!"#$%&'()* x13j14z

5 = = =!"#$% 282 = !"#$%&'()*+ Fig. 2 Dendrogram from Ward s method!"#$!"#$%&'()!"#$%&'()*+,-./01! 4 5!"#$%&#'( 4=! Table 4 Results of cluster analysis =! 1 3 == 2 4 === 3 3 == 4 5 ==== 5 3 = = 6 12 ===== ===== ==== 12!"#$%&!"#$% 1!"#$% 3!"!"#$%&'() GDP 4!!"5!!"#$%!"#$%&'()*+,-.!"#$%&'()*+,-./! 2!"#$% 4!"!"#$%&'()*!!"#$%&2005!"#$% %!"#$%&' 9000 CO 2!"#$%&'()!"#$%&'!"#$!"#$!"#$%!"1!"#$%!"#$%&!"GDP!"#!"#$%&!"#$%&'()*+,-./0123 ==== 34!"#$%&'()%*+,- 3!"#$%&'()*!"#$!"#$%&'()*+,!" 3!"#!"#$%&'(2!"#$%&'($) 2!"!!"#$%&4!"#$% 5!!"#$% &'()*+!"#$"%!"#$%&' ==== 56!"#$%&'()*!!"#$%!"#$%&5!!"#$%&'()*+!"#$%!"#$!"#$%&'(!!"#$%!"#$%&'()*+!"!"#$%6!"#$% 5=!"#$ Table 5 Cluster centroids (numbers in bold represent a higher level than national average)! N P Q R S! GDP/ / !" / ! /t CO 2 / !" / t CO GDP /% !"#$"%& /% !"#$%& /t / !"#$!"#$%&2005!"#$%&'(&)

6 4 =!"#$%&'()"*+, 283!"#$%&'!"#$%!!!"#$%&'()*+,!"#$%&!" CO 2!"#!"#$ OKO==!"#$%&'()*+ ====!"!"#$%&!"# 6 6=!"#$%& Table 6 Allocation results of CO 2 intensity reduction target =% = ! ! ! ====!"#$!"#!"#$ 12!"#$%&'()*+!"! 55% 65%! 3! 50% 12!!"#$%!"#$%&'!"#$%&'(!"#$%&'()*+,'-!"!"#$!"#$%&'()!"#$%&'!"#$%&'()!"#$%&'()*+,!"!"#$%&'()*+,-.#$/01!"#$%&'(!"#$%!"#$!"#$%&'()*+,!"#$%&'()!"# $%&' 3! 3!"#$%&'()*+,- 50%!"#$%&'()*+,-.!"#$%&'()*+!"!! 12!"#$ ====!"#$5!"#$%&'( 61%!36! 48%40%!"#$%&5!"#$%&'()*!"#$%&'()*+!"#$%!"#$%#&'()!"#$%&!"#$%&'()6!"#$!"#$%&'()*+,-%*.!!!"!!!!"#$%&'()*+,!"#$%&'()*+,!"#$!!"#$%!"#$!!"#$%&'!"#$%&'()!"!"#$%&'()*!"!"#$%&'!"#$!"#$%&'()*!"#$%&!"#$%&'()*+!"#!"#$%&'()*+!"#$!"#$#%&'(!"#$%&'!"#$%&'()* ====!"#$%&'()*+,-.!"#$!"#$%&'()*+5!"#$%& 54%!"#$%!"#$% 23! 49% 4!"#$%&!"#$%&'()!" #6!"#$%&'()!"#$ %!"#$%&'()!"#$%&'()!"#$%&! P=== ====!"#$%&'()*+,-#./0!"7!!"#$!%&'!!"#$%&'()*!!" 30!" 6!"#$%!!"#$%& 356!"!"!"#$%&'()*+,-(.!"#$%!&'()*&+,-./012!"#$%&'()*+,-./0123!!"#$%&'()*+,-./ ====!"#$!"#$1 2!"#$%&'()!"#$%& 55%65%!"#$%&'()*+,-.

7 = = =!"#$% 284 =2012!"#$%&'()*+,-.%,/012!"#$!"#$% 35!"!"#$%&!"#$48%61%!"#$%&'(!"#$%&'(!"#$%&'()*+,-!"#!"#$%&'()*+,-!"#!"#$%&'()*!"#$%&!"#$%&'(!"!"#!"#$% 5!!"#$%& 23!"#$%&'()*+! 54%49% 49%! [1] Yi Wenjing, Zou Lele, Guo Jie, et al. How can China reach its CO 2 intensity reduction target by 2020? A regional allocation based on equity and development [J]. Energy Policy, 2011, 39: 2407J2415 [2],,. 2020!"#$%&'()* [J]., 2011, 2: 36J40 [3].!"!"#$%&'(=[EB/OL] [ ] htm [4] 21.!"!"#$%&'() 20% [EB/OL] [ ]. znmdawmdizmzmzng.html [5] Punj G, Stewart D W. Cluster analysis in marketing research: review and suggestions for application [J]. Journal of Marketing Research, 1983, 20 (2): 134J148 [6] Soares J O, Marques M L, Monteiro C F. A multivariate methodology to uncover regional disparities: a contribution to improve European Union and governmental decisions [J]. European Journal of Operational Research, 2003, 145: 121J135 [7] Campo C D, Monteiro C F, Soares J O. The European regional policy and the socio-economic diversity of European regions: a multivariate analysis [J]. European Journal of Operational Research, 2008, 187: 600J612 [8] Ward J. Hierarchical grouping to optimize an objective function [J]. Journal of the American Statistical Association, 1963, 58: 236J244 [9] Marklund P O, Samakovlis E. What is driving the EU burden-sharing agreement: efficiency or equity? [J]. Journal of Environmental Management, 2007, 85: 317J9 [10] Ringius L, Torvanger A, Holtsmark B. Can multi-criteria rules fairly distribute climate burdens? OECD results from three burden sharing rules [J]. Energy Policy, 1998, 26: 777J793 [11] IPCC IPCC guidelines for national greenhouse gas inventories [M]. Tokyo: IGES, 2006 [12]!".!"#$% 2006 [M]. :!"#$, 2007 [13] Meng Lei, Guo Ju e, Chai Jian, et al. China s regional CO 2 emissions: characteristics, inter-regional transfer and emission reduction policies [J]. Energy Policy, 2011, 39: 6136J6144 [14],,,.!"#$%&'()*+,-.!"=[J].!"#$%, 2011, 7 (1): 35J40 Regional Allocation of CO 2 Intensity Reduction Targets Based on Cluster Analysis Yang Yuan 1, Cai Wenjia 1, Wang Can 1, Wang Siqiang 1, 2 1 School of Environment, Tsinghua University, Beijing , China; 2 Bureau of Energy, Beijing , China Abstract: To meet China s CO 2 intensity target of 40%J45% reduction by 2020 from 2005 level, a regional allocation method based on cluster analysis was developed. The 30 provinces were classified into 6 groups based on economy, emission and reduction potential indicators. Under equity principle, the two most developed groups are assigned the highest reduction targets (55% and 65% respectively), however their reduction potential is limited. Under efficiency principle, the 2 groups with highest reduction potential take the highest targets (48% and 61% respectively), but their economy is relatively backward. When equity and efficiency are equally weighted, the 5th group with prominent reduction potential takes the highest target (54%), and the 2nd and 3rd groups with large industry scale take the second highest target (49%). Whereas under the 3 allocation schemes, the targets are not greater than 40% for the 4th and 6th group, which have relatively low economic ability, emission and reduction potential. Due to inconsistency between economic ability and reduction potential, corresponding market mechanism and policy instrument should be established to ensure equity and efficiency of regional target allocation. Key words: CO 2 intensity reduction target allocation; cluster analysis; regional difference