A novel geographic evolution tree based on econometrics for analyzing regional differences in determinants of Chinese CO2 emission intensity

2022 ◽  
Vol 305 ◽  
pp. 114402
Author(s):  
Yannan Zhou ◽  
Yu Yang ◽  
Siyou Xia
2011 ◽  
Vol 88 (12) ◽  
pp. 4496-4504 ◽  
Author(s):  
Zhongfu Tan ◽  
Li Li ◽  
Jianjun Wang ◽  
Jianhui Wang

Energy Policy ◽  
2017 ◽  
Vol 109 ◽  
pp. 650-658 ◽  
Author(s):  
Shusen Gui ◽  
Chunyou Wu ◽  
Ying Qu ◽  
Lingling Guo

2021 ◽  
Vol 93 ◽  
pp. 105053
Author(s):  
Danyang Zhang ◽  
Hui Wang ◽  
Andreas Löschel ◽  
Peng Zhou

2020 ◽  
Vol 12 (5) ◽  
pp. 2148 ◽  
Author(s):  
Jingyao Peng ◽  
Yidi Sun ◽  
Junnian Song ◽  
Wei Yang

It is a very urgent issue to reduce energy-related carbon emissions in China. The three northeastern provinces (Heilongjiang (HLJ), Jilin (JL), and Liaoning (LN)) are typical heavy industrial regions in China, playing an important role in the national carbon emission reduction target. In this study, we analyzed the energy consumption, carbon dioxide (CO2) emissions, and CO2 emission intensity of each sector in the three regions, and we compared them with the national level and those of China’s most developed province Guangdong (GD). Then, based on an input–output (I–O) framework, linkage analysis of production and CO2 emission from sector–system and sector–sector dimensions was conducted. The results showed that the three regions accounted for about 1/10 of China’s energy consumption and 1/6 of China’s CO2 emissions in 2012. In addition, the level of energy structure, CO2 emission intensity, and sectoral structure lagged behind China’s average level, much lower than those for GD. According to the sectoral characteristics of each region and unified backward/forward linkages of production and CO2 emissions, we divided sectoral clusters into those whose development was to be encouraged and those whose development was to be restricted. The results of this paper could provide policy–makers with reference to exploring potential pathways toward energy-related carbon emission reduction in heavy industrial regions.


2021 ◽  
Author(s):  
He Huang ◽  
Qiushi Deng ◽  
Liang Li

Abstract BackgroundWith the economic development, China has become the world's largest CO2 emitter. Given that climate warming has increasingly become the focus of the international community, Chinese government committed to reducing its CO2 emission intensity substantially. Prior studies find that the evolution of economic structure and technological progress can reduce CO2 emissions, but lack of considering CO2 emissions and output as a whole. In addition, the role of education expenditure is relatively overlooked. This paper contributes to the literature by examining the link of CO2 emission intensity, non-renewable energy consumption and education expenditure in China during 1971-2014. ResultsWe use the ARDL approach and find that in the long run, every 1% increase in non-renewable energy consumption results in a 0.92% increase in CO2 intensity, while every 1% increase in operational education expenditure reduces the CO2 intensity by 0.86%. In the short term, 36% of the deviation from the long run equilibrium is corrected in the next period.ConclusionsWe draw out two important conclusions and make important policy recommendations. First and foremost, as long as the increase in operational educational expenditure exceeds the increase in non-renewable energy consumption, CO2 intensity of real GDP will decrease in the long run. This means that in the development stage when economic activities are still highly dependent on non-renewable energy sources, the Chinese government should continue to vigorously increase expenditures on public education. Second, the increase in non-renewable energy consumption will result in an increase in CO2 intensity of real GDP. Therefore, gradually increasing the proportion of clean energy consumption in the energy nexus is another powerful starting point for China to achieve its goal of reducing CO2 intensity of real GDP.JEL ClassificationC32. I2. Q4. Q53. Q56.


Sign in / Sign up

Export Citation Format

Share Document