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 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 Decoupling of Carbon Emissions from Economic Growth: An Empirical Analysis Based on 264 Prefecture-Level Cities in China

2021 ◽  
Author(s):  
Wenmei KANG ◽  
Benfan LIANG ◽  
Keyu XIA ◽  
Fei XUE ◽  
Yu LI
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Low Carbon ◽  
Per Capita Gdp ◽  
Plan Period ◽  
Per Capita

After setting the goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, it has become an irresistible trend for China to decouple carbon emissions from its economic growth. Since cities play a central role in reducing carbon emissions, the issues such as whether and when their carbon dioxide emissions can be decoupled from economic growth have become the focus of attention. Based on the carbon dioxide emissions of 264 prefecture-level cities in China from 2000 to 2017, this paper uses the Tapio decoupling index to measure the decoupling relationship between carbon emissions and economic growth of cities, analyzes the space–time evolution characteristics of carbon emissions and decoupling indexes by stages, and explores the relationship between carbon emissions and socio-economic development characteristics such as per capita GDP and industrial structure. The main conclusions drawn therefrom are as follows: (i) From 2000 to 2017, the city-wide carbon emissions rose from 2.484 billion tons in 2000 to 7.462 billion tons in 2017, registering a total increase of 200.40%. But the growth rate of carbon emissions within cities has been significantly reduced. (ii) As years passed by, the number of cities that achieved strong decoupling saw a significant increase, from zero in the 10th–11th Five-Year Plan period to 14 in the 12th Five-Year Plan period and the first two years of the 13th Five-Year Plan period, accounting for 5.3% of the total number of cities. (iii) There is an inverted U-shaped curve relationship between per capita carbon emissions and per capita GDP, which is consistent with the EKC curve hypothesis, but Chinese cities are still in the growth stage of the quadratic curve currently. The correlation between per capita CO2 emission and the proportion of the secondary industry was positive. The results of this study are expected to provide experience for the low-carbon development of cities in China and other developing countries, and provide references for the formulation and evaluation of policies and measures related to low-carbon economic development based on the decoupling model.

The Scenario Analysis of Carbon Emissions Based on Improved IPAT Model in China

2012 ◽  
Vol 616-618 ◽  
pp. 1484-1489 ◽  
Author(s):  
Xu Shan ◽  
Hua Wang Shao
Keyword(s):  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Energy Structure ◽  
Low Carbon ◽  
Policy Scenario ◽  
Social And Economic Development ◽  
Energy Environment ◽  
Development Goals

The coordination development of economy-energy-environment was discussed with traditional environmental loads model, combined with "decoupling" theory. Considering the possibilities of social and economic development, this paper set out three scenarios, and analyzed quantitatively the indexes, which affected carbon dioxide emissions, including population, per capita GDP, industrial structure and energy structure. Based on this, it forecasted carbon dioxide emissions in China in future. By comparing the prediction results, it held that policy scenario was the more realistic scenario, what’s more it can achieve emission reduction targets with the premise of meeting the social and economic development goals. At last, it put forward suggestions to implement successfully policy scenario, from energy structure, industrial structure, low-carbon technology and so on.

scholarly journals The Potential For Economic Growth And Carbon Dioxide Emissions Reduction: Using Input-Output Framework

2020 ◽  
Vol 214 ◽  
pp. 01038
Author(s):  
Lihao Sun ◽  
Yuxiang Shen
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Economic Structure ◽  
Emissions Reduction ◽  
Heat Power ◽  
Input Output ◽  
Key Factor ◽  
The Status ◽  
Emissions Intensity

As people’s living standards continue to ameliorate, people become more and more demanding of the status of eco-environment, and carbon emissions are a key factor affecting the eco-environment. We analyze the carbon emissions intensity and carbon emissions potential of different sectors in China based on the input-output model. The results show that the sector of Production and Supply of Electric Power and Heat Power has the highest embodied carbon emissions intensity because the sector provides the country with necessary electricity and heat power for its economic growth. In addition, this paper determines the key carbon emissions sectors using elasticity method, and the results show that Construction is the most influential carbon emissions sector in the future. By restricting key carbon emissions sectors and encouraging the non-key carbon emissions sectors, we can take into account both economic development and carbon emissions reduction with the multi-objective model. The results show that under the present economic scale of China, carbon emissions can decrease from 11591 million ton to 11011 million ton, with a difference of 580 million ton. This indicates that with the assurance of present economic growth, we can achieve the goal of reducing carbon emissions by adjusting the economic structure. Based on results of this paper, we have also made recommendations for adjusting the economic structure to achieve emission reduction targets.

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.

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.

scholarly journals Establishment and research of combined forecasting model of carbon

2021 ◽  
Vol 237 ◽  
pp. 01008
Author(s):  
Zuxu Zou ◽  
Jiaojiao Huang ◽  
Chenlu Li
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Programming Model ◽  
Forecasting Model ◽  
Coal Consumption ◽  
Combination Forecasting ◽  
Polynomial Curve ◽  
The Status

This paper analyzes the influencing factors of carbon dioxide emissions from four aspects: Population, economy, industrial structure and energy, then from the carbon emissions, economic development, industrial structure, energy consumption structure to show the status quo of carbon emissions in Hubei Province. Based on the analysis of the influencing factors, the main influencing factors of carbon emission are population, regional gross product and coal consumption The multivariate linear regression model and the polynomial curve model are established and the error analysis is carried out. The combination weight coefficients of two single models are obtained through the linear programming model and the combination forecasting model is established, finally, the corresponding countermeasures to reduce carbon emissions are put forward.

scholarly journals Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

2019 ◽  
Vol 11 (3) ◽  
pp. 406-423 ◽  
Author(s):  
Lei Wen ◽  
Linlin Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Projection Pursuit ◽  
Energy Structure ◽  
Per Capita Gdp ◽  
Content Type ◽  
Factors Affecting ◽  
Optimal Projection ◽  
Per Capita

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

scholarly journals Scenario Analysis of Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

2019 ◽  
Vol 11 (15) ◽  
pp. 4220 ◽  
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Minjin Zhan ◽  
Qamar Munir ◽  
Karthikeyan Brindha ◽  
...  
Keyword(s):  
Scenario Analysis ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Gdp Per Capita ◽  
Energy Structure ◽  
Urbanization Level ◽  
Autonomous Region ◽  
Per Capita ◽  
Xinjiang Autonomous Region

The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990–2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030, 2030, 2040, and 2040, respectively. At last, under the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.

The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis

2020 ◽  
Vol 8 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Jichang Dong ◽  
Jing He ◽  
Xiuting Li ◽  
Xindi Mou ◽  
Zhi Dong
Keyword(s):  
Carbon Dioxide ◽  
Sustainable Development ◽  
Carbon Dioxide Emissions ◽  
Service Sector ◽  
Energy Use ◽  
Industrial Structure ◽  
Negative Impact ◽  
Structure Change ◽  
Per Capita ◽  
Cross Country

AbstractReduction of carbon dioxide (CO2) emissions is one of the biggest challenges for global sustainable development, in which economic growth characterized by industrialization plays a formidable role. We innovatively adopted the input and output (I-O) table of 41 countries released by World I-O Database to determine the industrial structure change and analyze its impact on CO2 emission evolution by developing a cross-country panel model. The empirical results show that industrial structure change has a significantly negative effect on CO2 emissions; to be specific, 0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO2 emissions, indicating that upgrading industrial structure contributes to carbon mitigation and sustainable development. Further, urbanization, technology and trade openness have significantly negative impact on CO2 emissions, while economy growth and energy use take positive impacts. In particular, a 1% increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO2 emissions. However, the effect of industrial structure on environment degradation is moderated by technology level. These findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO2 emissions and achieve sustainable development.

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