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| 1 |
ID:
182723
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| Summary/Abstract |
One of the most undesirable output of China's rapid economic growth has been increasing carbon emissions. This study measures and analyzes the impact of carbon emissions on China's regional total factor productivity from 2000 to 2017. Using global Malmquist-Luenberger productivity indexes, we re-estimate the provincial total factor productivity taking carbon emission into account, comparing different assumptions of returns to scale and considering the rank reverse issue. The differences of technical progress and efficiency change across Chinese regional economies are also investigated and we found that the former was the primary contributor to improved Chinese provincial productivity performance. In addition, we analyze the influencing factors of productivity based on provincial panel data. Our results indicate that innovation capacity, energy and employment structure had significant impact on the provincial productivities while urbanization had a negative impact. A more sustainable development can be expected by expanding regional investment in R&D, adjusting and optimizing structures of regional industries and energies.
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| 2 |
ID:
150014
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| Summary/Abstract |
Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist–Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed.
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