Energy and Pollution Efficiencies of Regions in China Jin-Li Hu National Chiao Tung University, Taiwan Tzu-Pu Chang Academia Sinica, Taiwan http://jinlihu.tripod.com 2013/11/26 1
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
Dual Challenges of China China s GDP has grown by almost 110 times from 1978 to 2010. 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
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
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
Total-factor energy efficiency (TFEE) - Hu and Wang (2006) 6
Total-factor pollution efficiency (TFPE) - Hu (2005) 7
CRS-SBM model with undesirable outputs by Cooper et al. (2007) 8 1 2 1 1 2 1 1,,, 1 1 1 1 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)
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
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
Chinese Regional Data in 2010 Variable Unit Mean S.D. GDP 100 million RMB 15,052.91 11,591.61 in 2010 SO2 ton 753,366.86 462,500.02 Energy 10,000 TCe 13,431.41 8,543.17 Labor 10,000 persons 811.61 562.92 Capital 100 million RMB 1,510.96 1,655.85 in 2010 Farm area 1000 ha 5,532.23 3,875.29 11
OTE, TFEE, and TFPE scores of Regions in China, 2010 ID Area OTE TFEE TFPE Beijing E 1.000 1.000 1.000 Tianjin E 1.000 1.000 1.000 Hebei E 0.486 0.516 0.409 Shanxi C 0.374 0.320 0.168 Inner Mongolia W 0.380 0.513 0.213 Liaoning E 0.667 0.590 0.509 Jilin C 0.434 0.611 0.555 Heilongjiang C 0.440 0.540 0.483 Shanghai E 1.000 1.000 1.000 Jiangsu E 0.734 0.962 0.910 Zhejiang E 0.895 0.942 0.858 Anhui C 1.000 1.000 1.000 Fujian E 1.000 1.000 1.000 Jiangxi C 0.449 0.870 0.387 Shandong E 0.611 0.776 0.627 Henan C 0.493 0.655 0.401 Hubei C 0.585 0.657 0.634 Hunan C 1.000 1.000 1.000 Guangdong E 1.000 1.000 1.000 Guangxi W 0.475 0.707 0.242 Hainan E 1.000 1.000 1.000 Sichuan W 1.000 1.000 1.000 Guizhou W 0.377 0.344 0.098 Yunnan W 1.000 1.000 1.000 Shaanxi W 0.457 0.706 0.305 Gansu W 0.243 0.407 0.171 Qinghai W 0.357 0.308 0.215 Ningxia W 0.312 0.268 0.124 Xinjiang W 0.332 0.384 0.211 12
Data in 2011 Variable Unit Mean S.D. GDP 100 million RMB 16,828.47 12,793.26 SO2 ton 764,651.93 469,668.76 Energy 10,000 TCe 14,562.24 9106.77 Labor 10,000 persons 913.47 618.36 Capital 100 million RMB 1,630.05 1,746.42 Farm area 1000 ha 5,587.65 3,896.61 13
OTE, TFEE, and TFPE scores of Regions in China, 2011 ID Area Efficiency ETFEE ETFPE Beijing E 1.000 1.000 1.000 Tianjin E 1.000 1.000 1.000 Hebei E 0.482 0.522 0.330 Shanxi C 0.384 0.319 0.124 Inner Mongolia W 0.390 0.504 0.204 Liaoning E 0.689 0.566 0.413 Jilin C 0.421 0.610 0.400 Heilongjiang C 0.440 0.541 0.374 Shanghai E 1.000 1.000 1.000 Jiangsu E 0.714 0.997 0.782 Zhejiang E 1.000 1.000 1.000 Anhui C 1.000 1.000 1.000 Fujian E 1.000 1.000 1.000 Jiangxi C 0.424 0.864 0.305 Shandong E 0.585 0.788 0.485 Henan C 0.471 0.649 0.326 Hubei C 0.573 0.642 0.527 Hunan C 0.762 0.736 0.716 Guangdong E 1.000 1.000 1.000 Guangxi W 0.487 0.704 0.345 Hainan E 1.000 1.000 1.000 Sichuan W 1.000 1.000 1.000 Guizhou W 0.393 0.342 0.093 Yunnan W 1.000 1.000 1.000 Shaanxi W 0.413 0.665 0.211 Gansu W 0.249 0.402 0.124 Qinghai W 0.354 0.270 0.164 Ningxia W 0.299 0.247 0.077 Xinjiang W 0.325 0.345 0.134 14
OTE, TFEE, TFPE scores of regions in China, 2010 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Efficiency TFEE TFPE Year 2010 2010 E 2010 C 2010 W 15
OTE, TFEE, TFPE scores of regions in China, 2011 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Efficiency TFEE TFPE Year 2011 2011 E 2011 C 2011 W 16
Overall Technical Efficiency 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 2010 2011 OTE E C W 17
OTE efficiency score rankings East (0.854), central (0.597), and west (0.493) in 2010. East (0.861), central (0.559), and west (0.491) in 2011. 18
Total-factor Energy Efficiency 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 2010 2011 TFEE E C W 19
TFEE score rankings East (0.890), central (0.706), and west (0.564) in 2010. East (0.898), central (0.670), and west (0.548) in 2011. 20
TFPE 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 2010 2011 TFPE E C W 21
TFPE score rankings East (0.847), central (0.579), and west (0.358) in 2010. East (0.819), central (0.471), and west (0.335) in 2011. 22
Worsening Pollution Efficiency The pollution efficiency scores in regions of China are lower than those of energy efficiency during 2010-2011. This result is consistent with the improvement in Chinese regional energy intensities but worsening air pollution situations. 23
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
Chinese Regional Data in 2011 Variable Unit Mean S.D. GDP 100 million RMB 16,828.47 12,793.26 in 2010 SO2 ton 764,651.93 469,668.76 Energy 10,000 TCe 14,562.24 9106.77 Labor 10,000 persons 913.47 618.36 Capital 100 million RMB 1,630.05 1,746.42 in 2010 Farm area 1000 ha 5,587.65 3,896.61 25
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
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
Energy productivity changes of Chinese regions, 2010-2011 ID Area Malm growth Luen growth M-L growth Beijing E 0.038-0.072-0.075 Tianjin E 0.041-0.021-0.022 Hebei E 0.167 0.068 0.080 Shanxi C 0.173-0.040-0.045 Inner Mongolia W 0.183-0.024-0.024 Liaoning E 0.413 0.048 0.051 Jilin C 0.317 0.037 0.057 Heilongjiang C 0.201 0.019 0.029 Shanghai E 0.076-0.091-0.096 Jiangsu E 0.315 0.027 0.028 Zhejiang E 0.362-0.015-0.015 Anhui C 0.235-0.012-0.012 Fujian E 0.030-0.037-0.038 Jiangxi C -0.147 0.009 0.009 Shandong E 0.187 0.061 0.070 Henan C 0.093-0.012-0.014 Hubei C 0.253 0.018 0.026 Hunan C -0.422-0.199-0.200 Guangdong E 0.047-0.107-0.107 Guangxi W 0.124-0.140-0.139 Hainan E -0.094-0.057-0.059 Sichuan W 0.806-0.067-0.070 Guizhou W 0.293-0.150-0.132 Yunnan W -0.026 0.071 0.079 Shaanxi W -0.238 0.000 0.000 Gansu W 0.108 0.023 0.030 Qinghai W -0.291-0.021-0.034 Ningxia W -0.127 0.147 0.188 Xinjiang W -0.199 0.025 0.040 28
The effects of taking into account undesirable outputs Taking into account the undesirable outputs reduces the energy productivity changes for regions in China. 29
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