Green development performance and its influencing factors: A global perspective

Feng, Chao; Wang, Miao; Liu, Guanchun; Huang, Jianbai

doi:10.1016/j.jclepro.2017.01.005

Cited by 167 publications

(113 citation statements)

References 107 publications

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“…In this study, we utilized workers (employed persons) and capital stock as the inputs (Choi et al, 2012). Capital stock was calculated by the perpetual inventory method (Feng et al, 2017a). CO 2 emission was utilized as the undesirable output, and gross domestic product (GDP) was utilized as the desirable output.…”

Section: Data Sourcementioning

confidence: 99%

“…CEE was taken as the dependent variable for building the (Feng et al, 2017a;Lv et al, 2015;Yuan et al, 2013). The spatial spillover effect of CEE is the impact of some prefecture-level cities on their neighboring prefecture-level cities.…”

Section: Spatial Lag Panel Tobit Modelmentioning

confidence: 99%

“…Meanwhile, this study explored whether there was environmental Kuznets curve (EKC) for CEE and urbanization. Based on the above considerations, the model is as follows (Anselin, 1988;Feng et al, 2017a;Yuan et al, 2013):…”

Section: Spatial Lag Panel Tobit Modelmentioning

confidence: 99%

“…Furthermore, since GDP per capita (GDPP) is an important influence on energy consumption and CO 2 emissions , GDPP is also found to be closely related to CEE (Yuan et al, 2013). In addition, CEE may be affected by industrial structure and population density (Feng et al, 2017a;Yuan et al, 2013). Secondary industry is the main source of CO 2 emissions, and it is more difficult to improve the CEE of secondary industry (Hao et al, 2015).…”

Section: Variable and Software Selectionmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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

Huang

Kwan

et al. 2018

Journal of Cleaner Production

142

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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: Data Sourcementioning

confidence: 99%

Section: Spatial Lag Panel Tobit Modelmentioning

confidence: 99%

Section: Spatial Lag Panel Tobit Modelmentioning

confidence: 99%