scholarly journals Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016

2020 ◽  
Vol 17 (16) ◽  
pp. 5880
Author(s):  
Linlin Ye ◽  
Xiaodong Wu ◽  
Dandan Huang
Keyword(s):  
Co2 Emissions ◽  
Influencing Factors ◽  
Yangtze River ◽  
Energy Intensity ◽  
Driving Factors ◽  
Energy Structure ◽  
The Yangtze River ◽  
Economic Output ◽  
Industrial Energy ◽  
Economic Zone

As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008–2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity.

scholarly journals The Energy Efficiency and the Main Influencing Factors for the Logistics Industry in the Yangtze River Economic Belt in China

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xun Li ◽  
Chuan Lin
Keyword(s):  
Energy Efficiency ◽  
Technical Efficiency ◽  
Influencing Factors ◽  
Yangtze River ◽  
Scale Efficiency ◽  
Energy Structure ◽  
The Yangtze River ◽  
Service Capacity ◽  
Logistics Industry ◽  
East Central

Logistics is the booster of economic development, and it is imperative to further improve the logistics energy efficiency and adjust its development model. This paper is an attempt to investigate the logistics energy efficiency and main influencing factors of the Yangtze River Economic Belt, which is the most economically intensive region that spans East, Central, and West China. The input-oriented SBM-DEA model is employed to identify factors such as energy input, undesirable output, and service capacity output as well as the logistics energy efficiency of Yangtze River Economic Belt. Energy efficiency is then further decomposed into pure technical efficiency, scale efficiency, and technical efficiency, from the perspective of which provinces and cities are compared. The research results show that the logistics energy efficiency of Yangtze River Economic Belt needs to be further improved and that energy efficiency differs greatly among cities and provinces, indicating that the development is quite unbalanced in different areas. Therefore, the local government should develop development strategies according to the main influencing factors and constraints to the local logistics industry, so as to optimize and upgrade the energy structure of the logistics industry.

scholarly journals Exploring the Driving Forces and Reduction Potential of Industrial Energy-Related CO2 Emissions during 2001–2030: A Case Study for Henan Province, China

2019 ◽  
Vol 11 (4) ◽  
pp. 1176 ◽  
Author(s):  
Lei Liu ◽  
Ke Wang ◽  
Shanshan Wang ◽  
Ruiqin Zhang ◽  
Xiaoyan Tang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Driving Forces ◽  
Henan Province ◽  
Policy Implications ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Industrial Emissions ◽  
Economic Output ◽  
Industrial Energy

In China, the industrial sector is the main contributor to economic development and CO2 emissions, especially for the developing regional provinces. This study employs the Logarithmic Mean Divisia Index (LMDI) approach to decompose industrial energy-related CO2 emission into eight factors during 2001–2015 for Henan Province. Furthermore, the future CO2 emissions under different scenarios (Business as Usual (BAU), Efficiency Improvement (EI), Structural Optimization (SO), R&D Input (RD), and Comprehensive Policy (CP) scenarios) over 2016–2030 are projected. The results indicate that among these factors, the economic output, R&D intensity, investment intensity, and energy structure are the drivers for increasing CO2 emissions over the entire period, with the contribution of 293, 83, 80, and 1% of the total CO2 emissions changes, respectively. Conversely, the energy intensity, R&D efficiency, and industrial internal structure can decrease CO2 emissions with contributions of –86, –163, and –108% to the changes, respectively. Under the five scenarios, CO2 emissions in 2030 will reach 1222, 1079, 793, 987, and 638 Mt with an annual growth rate of 4.7%, 3.8%, 1.8%, 3.3%, and 0.4%, respectively. In particular, the CO2 emission peak for SO and CP scenarios is observed before 2030. Finally, some policy implications are suggested to further mitigate industrial emissions.

scholarly journals Impact of Influencing Factors on CO2 Emissions in the Yangtze River Delta during Urbanization

2019 ◽  
Vol 11 (15) ◽  
pp. 4183 ◽  
Author(s):  
Yixi Xue ◽  
Jie Ren ◽  
Xiaohang Bi
Keyword(s):  
Co2 Emissions ◽  
Yangtze River ◽  
Energy Intensity ◽  
Yangtze River Delta ◽  
Gdp Per Capita ◽  
The Yangtze River ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Yangtze River Delta

The Yangtze River Delta (YRD) is China’s largest urban agglomeration with a rapid urbanization process. This paper analyzes the dynamic relationship between urbanization rate, energy intensity, GDP per capita, and population with CO2 emissions in YRD over 1990–2011 based on the extended STIRPAT model, impulse response function, and variance decomposition. A support vector machine model was constructed to further predict the scenarios of YRD’s CO2 emissions from 2015–2020. The results show that YRD’s CO2 emissions continuously increased during the sample period and are predicted to increase over 2015–2020. Energy intensity is the most influential factor, both in the short and long term, and the total population contributes the least. However, the influencing magnitude of energy intensity tends to decrease in the long term. The increase of urbanization rate is still accompanied by the increase of CO2 emissions in YRD, but an inverted-U shape relationship between them may exist in the long term. The contribution of GDP per capita to CO2 emissions is higher than the population and urbanization rate, and its contribution rate for CO2 emissions is growing. The Kuznets curve does not exist in the current YRD.

scholarly journals An Analysis of Disparities and Driving Factors of Carbon Emissions in the Yangtze River Economic Belt

2019 ◽  
Vol 11 (8) ◽  
pp. 2362 ◽  
Author(s):  
Decai Tang ◽  
Yan Zhang ◽  
Brandon J. Bethel
Keyword(s):  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Yangtze River ◽  
Energy Intensity ◽  
Driving Factors ◽  
The Yangtze River ◽  
Stirpat Model ◽  
Per Capita Gdp ◽  
Positive Effects ◽  
Per Capita

As one of the “three major strategies” for China’s regional development, the Yangtze River Economic Belt (YREB) is under severe pressure to reduce carbon dioxide emissions, this paper analyzes the spatiotemporal disparities, and driving factors of carbon emissions based on energy consumption and related economic development data in the YREB over the 2005–2016 11-year period. Using the Stochastic Impacts Regression on Population, Affluence and Technology (STIRPAT) model, we empirically test the factors affecting YREB carbon emissions and key drivers in various provinces and municipalities. The main findings are as follows. First, per capita GDP, both industrial structure and energy intensity have positive effects on increasing carbon emissions. Second, per capita GDP and energy intensity have the largest impact on the increase of carbon emissions, and the urbanization rate has the largest inhibitory effect on carbon emissions.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

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