This study explored spatial explicit multiple cropping efficiency （MCE） of China in 2005 by coupling time series remote sensing data with an econometric model - stochastic frontier analysis （SFA）. We firstly extra...This study explored spatial explicit multiple cropping efficiency （MCE） of China in 2005 by coupling time series remote sensing data with an econometric model - stochastic frontier analysis （SFA）. We firstly extracted multiple cropping index （MCI） on the basis of the close relationship between crop phenologies and moderate-resolution imaging spectroradiometer （MODIS） enhanced vegetation index （EVI） value. Then, SFA model was employed to calculate MCE, by considering several indicators of meteorological conditions as inputs of multiple cropping systems and the extracted MCI was the output. The result showed that 46% of the cultivated land in China in 2005 was multiple cropped, including 39% double- cropped land and 7% triple-cropped land. Most of the multiple cropped land was distributed in the south of Great Wall. The total efficiency of multiple cropping in China was 87.61% in 2005. Southwestern China, Ganxin Region, the middle and lower reaches of Yangtze River and Huanghuaihai Plain were the four agricultural zones with the largest rooms for increasing MCI and improving MCE. Fragmental terrain, soil salinization, deficiency of water resources, and loss of labor force were the obstacles for MCE promotion in different zones. The method proposed in this paper is theoretically reliable for MCE extraction, whereas further studies are need to be done to investigate the most proper indicators of meteorological conditions as the inputs of multiple cropping systems.展开更多
This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency （TFEE）. The East region has the best energy efficiency and the Central and the West regions stand as the second ...This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency （TFEE）. The East region has the best energy efficiency and the Central and the West regions stand as the second and the third respectively. However, it is found that industrial energy efficiency of all regions increased from 1998 to 2006. This result is consistent with level of economic development of every region. The industries of all provinces in China are not yet at the frontier efficiency position, therefore, to the frontier as target, their technol- ogy levels and production processes should be adjusted accordingly. Compared with the conventional energy efficiency, the inverse of energy intensity, which is defined as the ratio of actual output to energy input, is regarded as Single-Factor Energy Efficiency （SFEE） index. Although TFEE ranks are not changed for each region, they are different for each province. The comparative result also shows that the substitution among inputs （labor, capital stock, and energy） to produce the output is significant. The SFEE scores could be over-estimated if energy is taken as the single input in the production. Finally, we identified determining factors affecting industrial energy efficiency using Tobit model. The results indicate that an increase of per capita Gross Domestic Product （GDP）, the percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad, energy price and investment of scientific and technological activities for industry could be possible contributors and drivers to the industrial energy efficiency. However, increasing of heavy industry will lead to worse industrial energy efficiency.展开更多
基金supported by the National Natural Science Foundation of China (41001277)the National 973 Program of China (2010CB95090102)
文摘This study explored spatial explicit multiple cropping efficiency （MCE） of China in 2005 by coupling time series remote sensing data with an econometric model - stochastic frontier analysis （SFA）. We firstly extracted multiple cropping index （MCI） on the basis of the close relationship between crop phenologies and moderate-resolution imaging spectroradiometer （MODIS） enhanced vegetation index （EVI） value. Then, SFA model was employed to calculate MCE, by considering several indicators of meteorological conditions as inputs of multiple cropping systems and the extracted MCI was the output. The result showed that 46% of the cultivated land in China in 2005 was multiple cropped, including 39% double- cropped land and 7% triple-cropped land. Most of the multiple cropped land was distributed in the south of Great Wall. The total efficiency of multiple cropping in China was 87.61% in 2005. Southwestern China, Ganxin Region, the middle and lower reaches of Yangtze River and Huanghuaihai Plain were the four agricultural zones with the largest rooms for increasing MCI and improving MCE. Fragmental terrain, soil salinization, deficiency of water resources, and loss of labor force were the obstacles for MCE promotion in different zones. The method proposed in this paper is theoretically reliable for MCE extraction, whereas further studies are need to be done to investigate the most proper indicators of meteorological conditions as the inputs of multiple cropping systems.
文摘This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency （TFEE）. The East region has the best energy efficiency and the Central and the West regions stand as the second and the third respectively. However, it is found that industrial energy efficiency of all regions increased from 1998 to 2006. This result is consistent with level of economic development of every region. The industries of all provinces in China are not yet at the frontier efficiency position, therefore, to the frontier as target, their technol- ogy levels and production processes should be adjusted accordingly. Compared with the conventional energy efficiency, the inverse of energy intensity, which is defined as the ratio of actual output to energy input, is regarded as Single-Factor Energy Efficiency （SFEE） index. Although TFEE ranks are not changed for each region, they are different for each province. The comparative result also shows that the substitution among inputs （labor, capital stock, and energy） to produce the output is significant. The SFEE scores could be over-estimated if energy is taken as the single input in the production. Finally, we identified determining factors affecting industrial energy efficiency using Tobit model. The results indicate that an increase of per capita Gross Domestic Product （GDP）, the percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad, energy price and investment of scientific and technological activities for industry could be possible contributors and drivers to the industrial energy efficiency. However, increasing of heavy industry will lead to worse industrial energy efficiency.
