Share:


Modelling technological bias and productivity growth: a case study of China’s three urban agglomerations

    Ke Li Affiliation
    ; Jianying Qu Affiliation
    ; Pan Wei Affiliation
    ; Hongshan Ai Affiliation
    ; Pinrong Jia Affiliation

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  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.

Keyword : total factor productivity, technological progress bias, Malmquist-Luenberger productivity index, data envelopment analysis, urban agglomeration

How to Cite
Li, K., Qu, J., Wei, P., Ai, H., & Jia, P. (2020). Modelling technological bias and productivity growth: a case study of China’s three urban agglomerations. Technological and Economic Development of Economy, 26(1), 135-164. https://doi.org/10.3846/tede.2020.11329
Published in Issue
Jan 3, 2020
Abstract Views
1456
PDF Downloads
734
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.