Green innovation effect of emission trading policy on pilot areas and neighboring areas: An analysis based on the spatial econometric model

Energy Policy ◽  
2021 ◽  
Vol 156 ◽  
pp. 112431
Author(s):  
Gang Du ◽  
Meng Yu ◽  
Chuanwang Sun ◽  
Zhao Han
Keyword(s):  
Econometric Model ◽  
Emission Trading ◽  
Green Innovation ◽  
Spatial Econometric ◽  
Spatial Econometric Model ◽  
Trading Policy

scholarly journals Spatial Econometric Model for Mapping Poverty Area in West Sulawesi

2021 ◽  
Vol 1752 (1) ◽  
pp. 012048
Author(s):  
Asrirawan ◽  
Rahmawati ◽  
Hikmah ◽  
M Abdy
Keyword(s):  
Econometric Model ◽  
Spatial Econometric ◽  
Poverty Area ◽  
Spatial Econometric Model

Factors Influencing China’s Exports with a Spatial Econometric Model

2015 ◽  
Vol 29 (3) ◽  
pp. 191-211 ◽  
Author(s):  
Kuang-Hann Chou ◽  
Chien-Hsun Chen ◽  
Chao-Cheng Mai
Keyword(s):  
Econometric Model ◽  
Spatial Econometric ◽  
Factors Influencing ◽  
Spatial Econometric Model

scholarly journals City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

2020 ◽  
Vol 17 (2) ◽  
pp. 652 ◽  
Author(s):  
Haisen Wang ◽  
Gangqiang Yang ◽  
Jiaying Qin
Keyword(s):  
Yangtze River ◽  
Econometric Model ◽  
Mediation Effect ◽  
Innovation Network ◽  
The Yangtze River ◽  
Green Innovation ◽  
Spatial Econometric ◽  
The Core ◽  
Influence Curve ◽  
The Impact

Based on the panel data of 106 cities in the Yangtze River Economic Belt of China from 2007 to 2016, this paper explores the impact of city centrality on the green innovation efficiency and proves the mediation effect of migrants by using spatial econometric model. The results show that there are more and more innovation contacts between cities, and the innovation network is becoming more and more dense. The core cities of the downstream innovation network are mainly Yangzhou, Zhenjiang, Wuxi, Changzhou, Suzhou and Hangzhou; the core cities in the midstream are mainly Wuhan, Changsha and Yichun; the core cities in the upstream are Chengdu and Bazhong. There is an inverted U-shaped relationship between city centrality and green innovation efficiency. In addition, the influence curve of city centrality on the green innovation efficiency of surrounding cities is also inverted U-shaped. Cities with high city centrality attract a large number of migrants that come from cities with lower centrality to improve the green innovation efficiency, but the green innovation efficiency of cities with low city centrality will decline due to lack of talents.

scholarly journals A Study on China's Energy Consumption by Bayesian Spatial Econometric Model

2012 ◽  
Vol 16 ◽  
pp. 1332-1340 ◽  
Author(s):  
YanpingYu ◽  
Xi Li ◽  
Zhengming Qian ◽  
Ming Yang ◽  
Zhi Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Econometric Model ◽  
Spatial Econometric ◽  
Spatial Econometric Model
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