An analysis of technological factors and energy intensity in China

By Junbing Huang, Dan Du, Qizhi Tao. 2017

182 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/3e232b7abaeacf100a522d350253e81bdec6fddc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Jun Liu
2. Hidemichi Fujii
3. Kun Yang
4. Liang Liu
5. Junbing Huang
6. Yu Hao
7. Qiang Liu
8. Xiaochen Cai

In Environmental Engineering

1. Jun Liu
2. Hidemichi Fujii
3. Kun Yang
4. Liang Liu
5. Yu Hao
6. Qiang Liu
7. Xiaochen Cai
8. Hongyan Lei

In Renewable Energy, Sustainability and the Environment

1. Jun Liu
2. Hidemichi Fujii
3. Kun Yang
4. Liang Liu
5. Yu Hao
6. Qiang Liu
7. Xiaochen Cai
8. Hongyan Lei

Citations in the corpus

• Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel (2021, methodology): “, 2019) and R&D level (Huang et al., 2017) as control variables in our investigation of the effect of AI on energy intensity.”
• The effect of technological factors on China's carbon intensity: New evidence from a panel threshold model (2018, background): “…and trade that may enhance the environmental quality were identified by existing studies (e.g., Ang, 2009; Perkins and Neumayer, 2011; Guo et al., 2012; Ren et al., 2014; Yang et al., 2014; Seker et al., 2015; Shao et al., 2016; Liu et al., 2017; Behera and Dash, 2017; Huang et al., 2017a, 2017b).”