Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach

Bian, Yiwen; Hu, Miao; Wang, Yousen; Xu, Hao

doi:10.1016/j.rser.2015.11.008

Cited by 55 publications

(32 citation statements)

References 50 publications

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“…In this study, urbanization (UR) was chosen as the main independent variable (Dong et al, 2016), and it was calculated as the percentage of the non-agricultural population to represent the urbanization level. The following variables were selected as control variables: GDP per capita (GDP divided by the population) (GDPP) (Feng et al, 2017a), industrial structure (the ratio of secondary industry GDP to total GDP) (IS) (Bian et al, 2016) and population density (the ratio of population to the area) (PD) (Liu et al, 2016b).…”

Section: Variable and Software Selectionmentioning

confidence: 99%

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Huang

Kwan

et al. 2018

Journal of Cleaner Production

132

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a b s t r a c tCities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000e2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries.

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Section: Variable and Software Selectionmentioning

confidence: 99%

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Huang

Kwan

et al. 2018

Journal of Cleaner Production

132

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“…The H1b (hypothesis one b) and H2b (hypothesis two b) that we made are also confirmed. The rapid growth in energy consumption may be caused by lower energy efficiency, but previous research [54,55] has improved energy efficiency and has increased in recent years in China. Therefore, technological innovation improves energy efficiency, increases consumer demand for energy, and then increases energy consumption in the long run.…”

Section: Unit Root Test and Cointegration Testmentioning

confidence: 99%

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

2018

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This paper investigates the relationship between energy consumption and technological innovation using a dynamic panel data model and regional-level data from China for the period 1995-2012. In contrast to previous studies, it examines the short and long-run bilateral relationship between technological innovation and energy consumption. The results show that in the short run, technological innovation leads to an increase in energy consumption, while energy consumption has no significant effect on technological innovation. In the long run, however, energy consumption is positively and bilaterally related to technological innovation. These findings suggest that although technological innovation does not directly lead to a reduction in energy consumption as mentioned in the extant literature, it could help achieve sustainability through improving energy efficiency and developing energy structure for developing countries.

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“…Due to its practicability and simplicity, this method has been extensively applied to efficiency evaluation of different decision making units (DMUs) in the energy sector [16]. Zhou et al [17] present a detailed literature survey on DEA utilized in energy studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficiency assessment of wind farms in China using two-stage data envelopment analysis

Xiao

et al. 2016

Energy Conversion and Management

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Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach

Cited by 55 publications

References 50 publications

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

Efficiency assessment of wind farms in China using two-stage data envelopment analysis

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