Panel estimation for transport sector CO2 emissions and its affecting factors: A regional analysis in China
By Chuanguo Zhang, J. Nian. 2013
142 citations recorded by the source corpus.
Subfields: Automotive Engineering, Environmental Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/319620742a81504184ed494fd0edcde25375385d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Bingjie Xu
- Canh Phuc Nguyen
- Thanh Dinh Su
- Christian Brand
- Felicia Hui Ling Chong
- Christophe Schinckus
- Avik Sinha
- Buhari Doğan
In Environmental Engineering
- Umer Shahzad
- Bingjie Xu
- Boqiang Lin
- Buhari Doğan
- Avik Sinha
- Z. Fareed
- Canh Phuc Nguyen
- Thanh Dinh Su
In Renewable Energy, Sustainability and the Environment
- Umer Shahzad
- Bingjie Xu
- Boqiang Lin
- Avik Sinha
- Buhari Doğan
- Z. Fareed
- Canh Phuc Nguyen
- Thanh Dinh Su
Citations in the corpus
- Does Export product diversification help to reduce energy demand: Exploring the contextual evidences from the newly industrialized countries (2021, methodology): “Second, the FGLS estimator is considered as most reliable in panel data when time period is more than number of entities (T>N) (Reed and Ye, 2011; Zhang and Nian, 2013).”
- The Influence of Tourism on Income Inequality (2020, methodology): “Pool ordinary least squares (OLS), robust pool OLS, and feasible generalized least squares (FGLS) were also used for the robustness purpose6 (Reed and Ye 2011; Liao and Cao 2013; Zhang and Nian 2013).”
- Future energy use and CO2 emissions of urban passenger transport in China: A travel behavior and urban form based approach (2018, background): “In recent years, city level research, with the benefit of detail human mobility, land use, transport infrastructure, and traffic information, started to evaluate the effects from the built environment and TDM on transport energy growth via individuals’ travel behavioral change [16, 17, 19, 36, 37].”