scholarly journals Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

2018 ◽  
Vol 28 (4) ◽  
pp. 495-513 ◽  
Author(s):  
Wensong Su ◽  
Yanyan Liu ◽  
Shaojian Wang ◽  
Yabo Zhao ◽  
Yongxian Su ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Spillover Effects ◽  
Regional Inequality ◽  
Level Energy ◽  
Spatial Spillover ◽  
Factors Influencing ◽  
Spatial Spillover Effects

scholarly journals Spatial Spillover Effects of Renewable Energy on Carbon Emissions in Less-developed Areas of China

2021 ◽  
Author(s):  
Jun Bai ◽  
Shixiang Li ◽  
Qiying Kang ◽  
Nan Wang ◽  
Kailu Guo ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Carbon Emissions ◽  
Spillover Effects ◽  
Spatial Spillover ◽  
Spatial Spillover Effects

scholarly journals Spatial Spillover Effects of Renewable Energy on Carbon Emissions in Less-Developed Areas of China

2021 ◽  
Author(s):  
Jun Bai ◽  
Shixiang LI ◽  
Qiying Kang ◽  
Nan Wang ◽  
Kailu Guo ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Carbon Emissions ◽  
Physical Capital ◽  
Spillover Effects ◽  
Moran’S I ◽  
Renewable Energy Consumption ◽  
Moran's I ◽  
Spatial Spillover ◽  
Carbon Emissions Reduction ◽  
Spatial Spillover Effects

Abstract The purpose of this paper is to determine the spatial spillover effects of renewable energy on carbon emissions in China’s less-developed areas. However, few studies have considered this issue from the perspective of less-developed areas. Based on panel data of 21 provinces in China from 2000 to 2017, this paper investigates the spatial spillover effects of renewable energy on carbon emissions using Moran’s I and Spatial Durbin Model (SDM). The results suggest that, first, Moran’s I ranges from 0.378 to 0.519, Moran scatter plot presents that provinces are located in the high–high (HH) and low-low (LL) quadrants, indicating provincial carbon emissions in the study area have a significant spatial correlation and agglomeration. Second, under the three matrices, the direct effect coefficients of renewable energy are − 0.2522, -0.2639 and − 0.2601, this shows that renewable energy is beneficial to local carbon emissions reduction. In contrast, the indirect effect coefficients of renewable energy are 0.0605, 0.1012 and 0.1125, which means higher renewable energy consumption in a single area is conducive to the improvement of carbon emissions to neighbouring areas. Third, urbanization, industrialization, physical capital and other variables have different impacts on local and nearby carbon emissions. This study provides empirical evidence to achieve carbon emission reduction targets by government policymakers.

scholarly journals Spatial Spillover Effects of Industrial Carbon Emissions in China

2018 ◽  
Vol 152 ◽  
pp. 679-684 ◽  
Author(s):  
Li Wenchao ◽  
Yan Yihui ◽  
Tian Lixin
Keyword(s):  
Carbon Emissions ◽  
Spillover Effects ◽  
Spatial Spillover ◽  
Industrial Carbon ◽  
Spatial Spillover Effects

Spatial spillover effects in determining China's regional CO 2 emissions growth: 2007–2010

2017 ◽  
Vol 63 ◽  
pp. 161-173 ◽  
Author(s):  
Bo Meng ◽  
Jianguo Wang ◽  
Robbie Andrew ◽  
Hao Xiao ◽  
Jinjun Xue ◽  
...  
Keyword(s):  
Spillover Effects ◽  
Spatial Spillover ◽  
Spatial Spillover Effects

Responding to Risky Neighbors: Testing for Spatial Spillover Effects for Defensible Space in a Fire-Prone WUI Community

2018 ◽  
Vol 73 (4) ◽  
pp. 1023-1047 ◽  
Author(s):  
Travis Warziniack ◽  
Patricia Champ ◽  
James Meldrum ◽  
Hannah Brenkert-Smith ◽  
Christopher M. Barth ◽  
...  
Keyword(s):  
Spillover Effects ◽  
Defensible Space ◽  
Spatial Spillover ◽  
Spatial Spillover Effects
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