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 Threshold Effect of Environmental Regulation, FDI Agglomeration, and Water Utilization Efficiency under “Double Control Actions”—An Empirical Test Based on Yangtze River Economic Belt

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 452 ◽  
Author(s):  
Xuhui Ding ◽  
Ning Tang ◽  
Juhua He
Keyword(s):  
Environmental Regulation ◽  
Yangtze River ◽  
Threshold Model ◽  
Utilization Efficiency ◽  
The Yangtze River ◽  
Per Capita Gdp ◽  
Water Utilization ◽  
Positive Effects ◽  
Per Capita ◽  
The Impact

In this study, the SE-SBM model considering undesirable outputs was used to measure the water utilization efficiency of the Yangtze River Economic Belt from 2006 to 2016, and the panel threshold model was used to estimate the impact of environmental regulation and foreign direct investment (FDI) agglomeration on water utilization efficiency. The results show that the water utilization efficiency presents a “U”-shaped trend as a whole, declines incrementally along the eastern, central, and western regions of the economic belt, and that the water utilization efficiency of the economic belt first converges and then diverges. In the estimation of the double threshold panel model, when the per capita GDP is lower than 2.635 or greater than 12.058 thousand dollars, the environmental regulation shows a significant positive effect. Otherwise, the environmental regulation barely shows a significant negative effect. FDI has not had a great impact on water resources utilization efficiency, and neither the “pollution aura” nor “pollution shelter” are significant. When the per capita GDP is lower than 2.184 or greater than 12.058 thousand dollars, FDI can significantly improve the water utilization efficiency through environmental regulation. Besides, the positive effects of technological innovation and foreign trade dependence are significant, and so are the negative effects of industrialization. Differentiated environmental regulation policies should be formulated; industrial upgrade should be promoted; innovation of water-saving and emission reduction should be strengthened in the Yangtze River Economic Belt.

scholarly journals Impact of Land Urbanization on Carbon Emissions in Urban Agglomerations of the Middle Reaches of the Yangtze River

2021 ◽  
Vol 18 (4) ◽  
pp. 1403
Author(s):  
Di Zhang ◽  
Zhanqi Wang ◽  
Shicheng Li ◽  
Hongwei Zhang
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Urban Expansion ◽  
The Yangtze River ◽  
Identity Model ◽  
Kaya Identity ◽  
Land Urbanization

The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.

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 Spatiotemporal Characteristics of Ecological Conditions and Its Response to Natural Conditions and Human Activities during 1990–2010 in the Yangtze River Delta, China

2018 ◽  
Vol 15 (12) ◽  
pp. 2910 ◽  
Author(s):  
Ziqi Meng ◽  
Min Liu ◽  
Qiannan She ◽  
Fang Yang ◽  
Lingbo Long ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Human Activities ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
The Yangtze River ◽  
Natural Conditions ◽  
Per Capita Gdp ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Yangtze River Delta

The Yangtze River Delta (YRD) region, including Shanghai City and the Jiangsu and Zhejiang Provinces, is the largest metropolitan region in China. In the past three decades, the region has experienced an unprecedented process of rapid and massive urbanization, which has dramatically altered the landscape and detrimentally affected the ecological environments in the region. In this paper, we analyzed the spatiotemporal variations of ecological conditions (Eco_C) via a synthetic index with analytic hierarchy processes in the YRD during 1990–2010. The relative contributions of influencing factors, including two natural conditions (i.e., elevation (Elev) and land-sea gradient (Dis_coa)), three indicators of human activities (i.e., urbanization rate (Urb_rate), per capita GDP (Per_gdp), the percentage of secondary and tertiary industry employment (Per_ind)), to the total variance of regional Eco_C were also investigated. The results showed that: (1) The Eco_C over YRD region was “Moderately High”, which was better than the national average and demonstrated obvious spatial variations between south and north. There existed fluctuations and an overall increasing trend for Eco_C during the study period, with 20% of the area being deteriorated and 40% being improved. (2) The areas with elevation below 10 m was relatively poor in Eco_C, while the regions above 1000 m showed the best Eco_C and had the most obvious changes (9.33%) during the study period. (3) The selected five influencing factors could explain 91.0–94.4% of the Eco_C spatial variability. Elevation was the dominant factor for about 42.4–52.9%, while urbanization rate and per capita GDP were about 32.5% and 9.3%.

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.

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 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.

Convergence of per capita carbon emissions in the Yangtze River Economic Belt, China

2018 ◽  
Vol 30 (5) ◽  
pp. 776-799 ◽  
Author(s):  
Shiwei Yu ◽  
Xing Hu ◽  
Xuejiao Zhang ◽  
Zhenxi Li
Keyword(s):  
Carbon Emissions ◽  
Yangtze River ◽  
The Yangtze River ◽  
Per Capita

scholarly journals Factors Influencing Indirect Carbon Emission of Residential Consumption in China: A Case of Liaoning Province

2019 ◽  
Vol 11 (16) ◽  
pp. 4414
Author(s):  
Yan Yan ◽  
Ancheng Pan ◽  
Chunyou Wu ◽  
Shusen Gui
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Liaoning Province ◽  
Urban Residents ◽  
Intensity Effect ◽  
Per Capita Gdp ◽  
Per Capita ◽  
Residential Consumption

Indirect carbon emissions caused by residential consumption has gradually become the key to the formulation of carbon emission reduction policies. In order to analyze the factors that influence the provincial residential indirect carbon emissions in China, comprehensive structural decomposition analysis (SDA) and logarithmic mean Divisia index (LMDI) models are established in this paper. The Liaoning province was selected due to its typical features as a province with higher urbanization rates. The model is based on input–output tables from 2002 to 2012, including those pertaining to the carbon emission coefficient (ΔF), energy intensity effect (ΔE), intermediate demand (ΔL), commodity structure (ΔS), residential consumption structure (ΔU), residential consumption ratio (ΔR), per capita GDP (ΔA) and population size (ΔP). The results show that the consumption of urban residents is the most common and significant section causing the growth of direct and indirect carbon emissions, both of which show an obvious upward trend. Nonmetal mining is the sector experiencing the greatest growth in indirect carbon emissions. The two most influential factors of indirect carbon emissions via the consumption of rural and urban residents are the intermediate demand effect (ΔL) and the per capita GDP effect (ΔA), respectively. Reducing energy intensity and optimizing commodity structures are the most effective ways to reduce indirect carbon emissions.

scholarly journals Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiekun Song ◽  
Qing Song ◽  
Dong Zhang ◽  
Youyou Lu ◽  
Long Luan
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Shandong Province ◽  
Stirpat Model ◽  
Per Capita Gdp ◽  
Consumption Structure ◽  
Per Capita ◽  
The Impact ◽  
Consumption Intensity

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great.

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