scholarly journals Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

2021 ◽  
Vol 13 (4) ◽  
pp. 1728
Author(s):  
Siyao Li ◽  
Qiaosheng Wu ◽  
You Zheng ◽  
Qi Sun
Keyword(s):  
Construction Industry ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Spatial Association ◽  
Policy Recommendations ◽  
Affecting Factors ◽  
Positive Effects ◽  
The Social ◽  
The Empirical Analysis

As the world’s largest carbon emitter, China is under enormous pressure to decrease carbon emissions. With the economic development in recent years, China has increased its investment in infrastructure, and the construction industry has become an essential source of carbon emissions. Using the social network analysis (SNA) methodology, this article analyzes the evolutionary characteristics of the spatial correlation network for carbon emissions in the construction industry from 2003–2017 and its affecting factors. The results of the empirical analysis in this paper are: (1) the spatial association of carbon emissions in Chinese inter-provincial construction industry shows an intuitive network layout and the spatial network has gradually stabilized since 2014; (2) according to the results of degree centrality, betweenness centrality and closeness centrality, it can be concluded that the regions with higher level of association with other provinces are the central and the eastern regions (Henan, Hubei, Hunan, Guangdong, Jiangsu, etc.) and Xinjiang; the linkage of construction-related carbon emissions was mainly achieved through the regions of Henan, Anhui, Shanxi, Hebei, Guangdong, and Inner Mongolia; the regions with higher level of construction industry development (Jiangsu, Henan, Hunan, Guangdong, etc.) are more closely associated with other provinces; (3) geographical proximity and reduction of difference in energy intensity and in industrial structure have substantial positive effects on the carbon emission association of the construction industry. Finally, based on the research results, this article proposes corresponding policy recommendations.

scholarly journals Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

2021 ◽  
Author(s):  
Haiying Liu ◽  
zhiqun zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Symbolic Regression ◽  
Car Ownership ◽  
Emissions Reduction ◽  
Policy Recommendations ◽  
The World ◽  
Urbanization Rate

Abstract Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emissions reduction; China is actively achieving emissions reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors, and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as type-EI, type-UR, type-GDP, type-IS, type-CO, or type-HCL regions. The decoupling index of each region showed a downward trend; EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.

scholarly journals The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1428 ◽  
Author(s):  
Na Lu ◽  
Shuyi Feng ◽  
Ziming Liu ◽  
Weidong Wang ◽  
Hualiang Lu ◽  
...  
Keyword(s):  
Construction Industry ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Energy Structure ◽  
Chinese Government ◽  
Low Carbon ◽  
Industry Agglomeration ◽  
Technology Innovations ◽  
Growth Energy

As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

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.

Research on Carbon Emissions Estimation and Decomposition of Industry in Hebei Province

2012 ◽  
Vol 518-523 ◽  
pp. 4941-4947
Author(s):  
Dong Heng Hao ◽  
Guo Zhu Li ◽  
Dian Ru Wang
Keyword(s):  
Carbon Emissions ◽  
Large Scale ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Hebei Province ◽  
Industrial Output ◽  
Factors Affecting ◽  
Industrial Enterprises ◽  
Industrial Energy ◽  
Major Factors

This paper estimated the carbon emissions of the large-scale industrial enterprises in Hebei Province, and analyzed their changing factors using the LMDI method. The results shows that major factors affecting the carbon emissions of industry in Hebei province are energy intensity, industrial structure and output changes . Seen from the absolute value , industrial output has the greatest impact on carbon emissions followed by energy intensity. Industrial structures has the least impact. The combined impact of industrial energy saving technological progress and industrial structure adjustment on carbon emissions is less than that of industrial output.

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 Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

2020 ◽  
Vol 17 (1) ◽  
pp. 357
Author(s):  
Ruyin Long ◽  
Qin Zhang ◽  
Hong Chen ◽  
Meifen Wu ◽  
Qianwen Li
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Northern China ◽  
Well Being ◽  
Spillover Effects ◽  
Local Area ◽  
Energy Structure ◽  
Positive Effects

Current energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem between energy consumption and residents’ health. In this paper, panel data of 30 provinces in mainland China from 2005 to 2016 is used to calculate the EIWB of each province and analyze its spatial distribution. Moreover, the effect of influencing factors on EIWB is investigated by using the spatial Durbin model. The results show that: (1) The EIWB presents a spatial agglomeration. The provinces with high EIWB mostly cluster in the northern China. (2) Industrial structure and energy structure have positive effects on EIWB in local area through increasing energy consumption and damaging residents’ health. (3) The effect of urbanization and income on local EIWB is significantly positive because it will promote energy consumption. (4) Industrial structure, health expenditure, foreign direct investment and technological progress have spatial spillover effects due to its significant impact on residents’ health in neighboring areas. Based on conclusions, the corresponding policy recommendations are proposed.

scholarly journals Regional Heterogeneity of Carbon Emissions and Peaking Path of Carbon Emissions in the Bohai Rim Region

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chuanhui Wang ◽  
Mengzhen Zhao ◽  
Weifeng Gong ◽  
Zhenyue Fan ◽  
Wenwen Li
Keyword(s):  
Renewable Energy ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
High Energy ◽  
Energy Utilization ◽  
Total Carbon ◽  
Energy Structure ◽  
Bohai Rim ◽  
Carbon Peak

Taking the Bohai Rim region as the research object and based on the relevant data of energy consumption, GDP, and energy structure from 2000 to 2019, the total carbon emissions of the provinces and cities from 2020 to 2050 were predicted. The carbon peak situation of each province and municipality in the Bohai Rim region was also analyzed. A comparative analysis of the peaks among the provinces and cities has been carried out. The results show the following: (1) it is predicted that Beijing will reach its carbon peak before 2025. Tianjin is predicted to reach its carbon peak before 2030. Renewable energy development and utilization technologies in the two municipalities are crucial to achieving carbon peaks when energy intensity is already low. (2) Shandong and Shanxi have a heavy energy structure, are coal-minded, and have high energy intensity, while the replacement rate of renewable energy is relatively low. Shandong and Shanxi are predicted to reach carbon peaks around 2030. Liaoning also has the problem of heavy industrial structure, and it is predicted to reach the carbon peak before 2027. (3) Hebei itself relies on Beijing, and its renewable energy utilization technology is relatively advanced. It is predicted to reach the carbon peak before 2026. The energy intensity of Inner Mongolia has decreased rapidly, and it is predicted to reach the carbon peak before 2029. Therefore, according to the forecast results and the analysis of the similarities and differences among the provinces and cities, some specific suggestions for the optimization of the energy structure and the development of renewable energy in each province and city have been proposed in order to promote the comprehensive realization of the regional carbon peak goal in the Bohai Rim region.

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.

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