scholarly journals The Empirical Study about the Impact of Industrial Structure Upgrade on Carbon Emissions Intensity in China

2013 ◽  
Vol 02 (04) ◽  
pp. 121-127
Author(s):  
青青 张
Keyword(s):  
Empirical Study ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Emissions Intensity ◽  
The Impact

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

Energy Consumption and Structural Reformation in Chinese Northeast Old Industrial Base

2013 ◽  
Vol 448-453 ◽  
pp. 4281-4284 ◽  
Author(s):  
Shao Bo Liu
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Energy Utilization ◽  
Energy Structure ◽  
Energy Technology ◽  
Industrial Base ◽  
Old Industrial Base ◽  
Emissions Intensity

Using IPCC methodology, the carbon emissions of Chinese Northeast Old Industrial Base is calculated, and the energy's synthesized impact on carbon emissions intensity is presented. The resulting shows that the carbon emissions in the three northeast provinces decreased 52.87% from 2000 to 2010, of which, Liaoning, Jilin and Heilongjiang are individually 60.09%, 45.47% and 54.14% lower. The implications are that the energy structure is one of the main factors in carbon emission in the Old Industrial Base of Northeast China, and its industrial structure is changing greatly due to energy consumption carbon emission. To adjust optimally the energy and industrial structure, and to develop the energy technology to promote energy utilization are recommended.

An Empirical Study on China’s Regional Carbon Emissions of Agriculture

2013 ◽  
Vol 4 (4) ◽  
pp. 67-77
Author(s):  
Li Pang ◽  
Jingyuan Zhao
Keyword(s):  
Energy Efficiency ◽  
Empirical Study ◽  
Carbon Emissions ◽  
Agricultural Development ◽  
Industrial Structure ◽  
Influential Factors ◽  
The West ◽  
Development Level ◽  
Labor Transfer

Based on China’s carbon emissions of agriculture, the authors appraise the area differentiation of carbon emissions of agriculture; examine the influential factors of agricultural carbon emissions in China. The results show that the performance of China’s agricultural carbon emissions is on the rise. The agricultural carbon emissions in the west of China increase rapidly. The area differentiation of agricultural carbon emissions in China decreases. In general, the major driver of carbon emissions is agricultural development level. Industrial structure, energy efficiency and labor transfer have significant effects on the performance of agricultural carbon emissions.

Measure of Carbon Emissions in Jiangsu Province and Analyses of Factors

2011 ◽  
Vol 71-78 ◽  
pp. 2262-2265
Author(s):  
Jian Hua ◽  
Jun Ren
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Jiangsu Province ◽  
Energy Structure ◽  
High Carbon ◽  
The Status ◽  
Per Capita ◽  
Emissions Intensity

We calculate the carbon dioxide emissions from the combustion of energy and production process of cement in Jiangsu Province from 1990 to 2009.Through the indicators such as carbon emissions intensity, per capita carbon emissions, we analyze the status and trends of carbon dioxide emissions in Jiangsu Province. Based on the factors of industrial structure, energy structure and high-carbon products, we give some suggestions.

scholarly journals Understanding the Impact of Land Resource Misallocation on Carbon Emissions in China

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1188
Author(s):  
Aihui Ma ◽  
Yaya He ◽  
Peng Tang
Keyword(s):  
Carbon Emissions ◽  
Local Governments ◽  
Industrial Structure ◽  
Coordination Mechanism ◽  
Kuznets Curve ◽  
Land Resource ◽  
Eastern Region ◽  
Land Resources ◽  
Resource Misallocation ◽  
The Impact

In order to achieve growth in fiscal revenue and the regional economy under the Chinese decentralization system, the land resources misallocation (LRM) among different industries was promoted through the differentiated land supply strategy, which has a vital role in carbon emissions. This study theoretically analyzes the overall effect and the effect of the intermediate LRM mechanism on carbon emissions and empirically tests the impact of LRM on carbon emissions based on panel data collected from 30 provinces in China from 2005 to 2017 using the environmental Kuznets curve theory. The results show that (1) the local governments have monopolized the primary land market across the nation, leading to resource misallocation among industrial, commercial, and residential land. This inefficient and unsustainable allocation aggravated the release of carbon emissions. (2) The impact of LRM on carbon emissions has varied among different regions. LRM in the eastern and central regions significantly exacerbated carbon emissions. A greater impact on carbon emissions occurred in the eastern region, while the impact was insignificant in the western region. (3) There are two mechanisms through which LRM affects carbon emissions. One is the restraint of upgrading industrial structure, and the other is the restriction of technological innovations. In conclusion, speeding up the reform of the tax sharing system is suggested to reduce the excessive dependence of local governments on land resources. Meanwhile, in order to reduce carbon emissions, the land acquisition and transfer system should be reformed to gradually achieve the market-oriented allocation of land resources, and the benefits coordination mechanism of different land transfer modes should be established. Finally, we propose different carbon emission reduction policies for the heterogeneity of regional economic development.

scholarly journals Research on the spatial effect and threshold effect of industrial structure upgrading on carbon emissions in China

2021 ◽  
Author(s):  
Zhenqiang Li ◽  
Qiuyang Zhou
Keyword(s):  
Energy Consumption ◽  
Population Size ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Threshold Effect ◽  
Local Areas ◽  
The Impact ◽  
Consumption Intensity ◽  
Industrial Structure Upgrading

Abstract Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened.

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