MODELLING TECHNOLOGICAL BIAS AND PRODUCTIVITY GROWTH: A CASE STUDY OF CHINA’S THREE URBAN AGGLOMERATIONS
The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chi...
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Published in | Technological and economic development of economy Vol. 26; no. 1; pp. 135 - 164 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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01.01.2020
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Abstract | The technological progress in favor of energy conservation and emission reduction will
help increase green total factor productivity and thus mitigate China’s environmental problems. This
study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP)
index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes
are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs
and outputs (including the undersirable output, measured by CO2 emissions) are estimated. Main
results are: (i) During the sample period, the TFP of the three urban agglomerations continues to
increase, and the main driving force is technological change. (ii) From the perspective of inputs, the
Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River
Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs,
the technological progress of the three major urban agglomerations is significantly biased toward
GDP with a slight difference among the three urban agglomerations, which means its technological
progress is conducive to reduce CO2 intensity, symbolizing low carbon development. From this
point of view, their economic growth shows a low-carbon trend. |
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AbstractList | The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend. The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend. The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by CO2 emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce CO2 intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend. |
Author | Wei, Pan Ai, Hongshan Jia, Pinrong Li, Ke Qu, Jianying |
Author_xml | – sequence: 1 givenname: Ke surname: Li fullname: Li, Ke organization: Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, P. R. China – sequence: 2 givenname: Jianying surname: Qu fullname: Qu, Jianying organization: Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, P. R. Chin – sequence: 3 givenname: Pan surname: Wei fullname: Wei, Pan organization: Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, P. R. China – sequence: 4 givenname: Hongshan surname: Ai fullname: Ai, Hongshan organization: School of Economy and Trade, Hunan University, Changsha, Hunan 410082, China – sequence: 5 givenname: Pinrong surname: Jia fullname: Jia, Pinrong organization: Beijing Research Center for Science of Science, Beijing 100054, China |
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help increase green total factor productivity and thus mitigate China’s... The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s... |
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SubjectTerms | Agglomeration Carbon Data envelopment analysis Economic development Emissions control Energy conservation Malmquist-Luenberger productivity index Operations research Productivity technological progress bias total factor productivity urban agglomeration |
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Title | MODELLING TECHNOLOGICAL BIAS AND PRODUCTIVITY GROWTH: A CASE STUDY OF CHINA’S THREE URBAN AGGLOMERATIONS |
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