Energy and Pollution Efficiencies of Regions in China
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1 Energy and Pollution Efficiencies of Regions in China Jin-Li Hu National Chiao Tung University, Taiwan Tzu-Pu Chang Academia Sinica, Taiwan /11/26 1
2 Dual Challenges of China China is facing a dual challenge of improving both energy and emission efficiencies. In addition to the rising energy consumption, the Asian Brown Cloud problem becomes more and more serious in north China. 2
3 Dual Challenges of China China s GDP has grown by almost 110 times from 1978 to China in 2010 became the world s biggest energy consumer with a whopping 20.3% share of global energy use (BP 2011). The economic loss caused by pollution has reached 1.4 trillion RMB, which accounted for 3.9% of GDP in 2008 (China Environmental and Economic Accounting Report, 2010). 3
4 Dual Challenges of China Energy + Pollution Efficiency Improvements Asian Brown Clouds (Hu et al., 2005) Imbalance of Regional Resource Efficiency and Productivity Total-factor Energy Efficiency (Hu and Wang, 2006) an EKC curve phenomenon in energy efficiency in Chinese regions 4
5 Dual Challenges of China Environmental Total-factor Energy Efficiency (Li and Hu, 2012) Taking pollution (SO2) into account makes the energy efficiency among Chinese regions more unbalanced. 5
6 Total-factor energy efficiency (TFEE) - Hu and Wang (2006) 6
7 Total-factor pollution efficiency (TFPE) - Hu (2005) 7
8 CRS-SBM model with undesirable outputs by Cooper et al. (2007) ,,, min n r n r b ro b r g ro g r m i io i s s s y s y s n n x s m b g (1) subject to S X x o (2) g g g o S Y y (3) b b b o S Y y (4) 0 0, 0, 0, 0,, 0 Y X s s s b g, (5)
9 Notations in the above DEA model where each region has m inputs, n 1 good outputs, and n 2 bad outputs; X, g Y, and b Y are the matrices of the input, good output, and bad output, respectively, while all of X, Y g, and b Y are strictly larger than zero; S, g S, and b S are the matrices of the input, good output, and bad output slacks, respectively; and is a constant vector. 9
10 Computing OTE, TFEE, and TFPE at the same time The above DEA model can generate target desirable output, target undesirable output, and target inputs at the same time. Therefore, we can compute the overall technical efficiency (OTE), TFEE, and TFPE scores for regions in China at the same time by one DEA model. 10
11 Chinese Regional Data in 2010 Variable Unit Mean S.D. GDP 100 million RMB 15, , in 2010 SO2 ton 753, , Energy 10,000 TCe 13, , Labor 10,000 persons Capital 100 million RMB 1, , in 2010 Farm area 1000 ha 5, ,
12 OTE, TFEE, and TFPE scores of Regions in China, 2010 ID Area OTE TFEE TFPE Beijing E Tianjin E Hebei E Shanxi C Inner Mongolia W Liaoning E Jilin C Heilongjiang C Shanghai E Jiangsu E Zhejiang E Anhui C Fujian E Jiangxi C Shandong E Henan C Hubei C Hunan C Guangdong E Guangxi W Hainan E Sichuan W Guizhou W Yunnan W Shaanxi W Gansu W Qinghai W Ningxia W Xinjiang W
13 Data in 2011 Variable Unit Mean S.D. GDP 100 million RMB 16, , SO2 ton 764, , Energy 10,000 TCe 14, Labor 10,000 persons Capital 100 million RMB 1, , Farm area 1000 ha 5, ,
14 OTE, TFEE, and TFPE scores of Regions in China, 2011 ID Area Efficiency ETFEE ETFPE Beijing E Tianjin E Hebei E Shanxi C Inner Mongolia W Liaoning E Jilin C Heilongjiang C Shanghai E Jiangsu E Zhejiang E Anhui C Fujian E Jiangxi C Shandong E Henan C Hubei C Hunan C Guangdong E Guangxi W Hainan E Sichuan W Guizhou W Yunnan W Shaanxi W Gansu W Qinghai W Ningxia W Xinjiang W
15 OTE, TFEE, TFPE scores of regions in China, Efficiency TFEE TFPE Year E 2010 C 2010 W 15
16 OTE, TFEE, TFPE scores of regions in China, Efficiency TFEE TFPE Year E 2011 C 2011 W 16
17 Overall Technical Efficiency OTE E C W 17
18 OTE efficiency score rankings East (0.854), central (0.597), and west (0.493) in East (0.861), central (0.559), and west (0.491) in
19 Total-factor Energy Efficiency TFEE E C W 19
20 TFEE score rankings East (0.890), central (0.706), and west (0.564) in East (0.898), central (0.670), and west (0.548) in
21 TFPE TFPE E C W 21
22 TFPE score rankings East (0.847), central (0.579), and west (0.358) in East (0.819), central (0.471), and west (0.335) in
23 Worsening Pollution Efficiency The pollution efficiency scores in regions of China are lower than those of energy efficiency during This result is consistent with the improvement in Chinese regional energy intensities but worsening air pollution situations. 23
24 Malmquist energy productivity index Without undesirable outputs TFP t t1 t1 t1 t1 t1 t1 t1 t t 1 Di( x, y ) Di ( x, y ) growth Mi( x, y, x, y ) t t t t1 t t Di( x, y ) Di ( x, y ) 1/2 D e x y t t t t 1 i ( o, mo, ro) min N t t s.t. e e, n1 N n1 N n1 n n n o t t x x, m 1,2,..., M, n mn mo t t y y, r 1,2,..., R, n rn ro 0, n1,2,..., N. 24
25 Chinese Regional Data in 2011 Variable Unit Mean S.D. GDP 100 million RMB 16, , in 2010 SO2 ton 764, , Energy 10,000 TCe 14, Labor 10,000 persons Capital 100 million RMB 1, , in 2010 Farm area 1000 ha 5, ,
26 Luenberger energy productivity index With undesirable outputs 1 TFP L D D 2 t1 t1 t1 t t t t t t t t t1 t1 t1 growth ( x, y, u x, y, u ) i( x, y, u ) i( x, y, u ) t i o mo ro ko e t t t t D ( e, x, y, u ; g ) max N n1 n1 n1 n1 t t s.t. e e g, N N N n n o e D ( x, y, u ) D ( x, y, u ), t1 t t t t1 t1 t1 t1 i i t t x x, m 1, 2,..., M, n mn mo t t y y, r 1, 2,..., R, n rn ro t t nukn uko, k 1, 2,..., K, n 0, n1, 2,..., N. 26
27 Malmquist-Luenberger energy productivity index With undesirable outputs TFP growth ML ( x, y, u, x, y, u ) i t1 t1 t1 t t t 1 t t t t t1 t t t 1 Di( x, y, u ) 1 Di ( x, y, u ) t t1 t1 t1 t1 t1 t1 t1 1 Di(,, ) 1 Di (,, ) x y u x y u 1/2 27
28 Energy productivity changes of Chinese regions, ID Area Malm growth Luen growth M-L growth Beijing E Tianjin E Hebei E Shanxi C Inner Mongolia W Liaoning E Jilin C Heilongjiang C Shanghai E Jiangsu E Zhejiang E Anhui C Fujian E Jiangxi C Shandong E Henan C Hubei C Hunan C Guangdong E Guangxi W Hainan E Sichuan W Guizhou W Yunnan W Shaanxi W Gansu W Qinghai W Ningxia W Xinjiang W
29 The effects of taking into account undesirable outputs Taking into account the undesirable outputs reduces the energy productivity changes for regions in China. 29
30 The following work More background (trend) introduction. Incorporating more emissions (such as CO2). Incorporating more regional industrial institutional factors. Taking into account more policy instruments. Proposing policy suggestions and green development strategies at the regional levels. 30
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