scholarly journals Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015

2018 ◽  
Vol 10 (8) ◽  
pp. 2809 ◽  
Author(s):  
Weidong Chen ◽  
Ruoyu Yang
Keyword(s):  
Panel Data ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spatial Association ◽  
Mainland China ◽  
Population Pressure ◽  
Spatial Trends ◽  
Investment In Fixed Assets

Based on provincial panel data from 2005 to 2016, this paper analyzes evolving temporal–spatial trends, spatial correlation and influencing factors of carbon emissions in China. The results show that there is a great heterogeneity in the evolving temporal–spatial trends of carbon emissions among provinces and regions in China, with the heterogeneity in eastern provinces most obvious. At the same time, there exists significant spatial correlation and agglomeration of carbon emissions in 30 provinces. It is found that the distribution characteristics of carbon emissions are affected by various economic and social factors based on the extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Population pressure, affluence, energy intensity, industrial structure, urbanization level and investment in fixed assets can significantly promote the increase of carbon emissions. The technological level and government environmental supervision have significant inhibitory effects on carbon emissions, but foreign direct investment (FDI) has no significant impact. Therefore, it is necessary to strengthen environmental supervision and upgrade technology level to promote carbon emission reduction.

scholarly journals Provincial Carbon Emissions Efficiency and Its Influencing Factors in China

2019 ◽  
Vol 11 (8) ◽  
pp. 2355 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Zhang ◽  
Dang
Keyword(s):  
Panel Data ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Scale Economy ◽  
Green Economy ◽  
Eastern Region ◽  
Tobit Regression ◽  
Emission Performance

We calculated provincial carbon emissions efficiency and related influencing factors in China with the purpose of providing a reference for other developing countries to develop a green economy. Using panel data covering the period from 2004–2016 from 30 provinces in China, we calculated the carbon emission performance (CEP) and the technology gap ratio of carbon emission (TGR) with the data envelopment analysis (DEA) method and the meta-frontier model separately to analyze provincial carbon emissions efficiency in China. No matter which indicator was employed, we found that distinct differences exist in the eastern, the central, and the western regions of China, and the eastern region has the highest carbon emission performance, followed by the central and the western regions. Then, the panel data Tobit regression model was employed to analyze the influencing factors of carbon emissions efficiency, and we found that scale economy, industrial structure, degree of opening up, foreign direct investment (FDI), energy intensity, government interference, ownership structure, and capital-labor ratio have different impacts on the carbon emission efficiency in different regions of China, which indicates different policies should be implemented in different regions.

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 Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1742 ◽  
Author(s):  
Ruoyu Yang ◽  
Weidong Chen
Keyword(s):  
Air Pollution ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Population Pressure ◽  
Spatial Data Analysis ◽  
So2 Emissions ◽  
Political Movement ◽  
The Impact

In order to study the present situation regarding SO2 emissions in China, problems are identified and countermeasures and suggestions are put forward. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.

Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1170
Author(s):  
Yameng Wang ◽  
Zhe Chen ◽  
Xiumei Wang ◽  
Mengyang Hou ◽  
Feng Wei
Keyword(s):  
Spatial Correlation ◽  
Industrial Structure ◽  
Science And Technology ◽  
Spatial Association ◽  
Spillover Effects ◽  
Agricultural Science ◽  
Allocation Efficiency ◽  
Development Levels ◽  
Agricultural Science And Technology ◽  
Technology Resources

The allocation efficiency of China’s agricultural science and technology resources (ASTR) varies in different regions and has a complicated spatial distribution pattern. To visually study whether there are correlations and mutual influences between the allocation efficiency of different regions, we use social network analysis methods (SNA). The study found that: (i) China’s allocation efficiency of ASTR has significant spatial correlation and spillover effects. The overall network density is declining. (ii) The spatial correlation network has significant regional heterogeneity. Some eastern provinces play “intermediaries” and “bridges” in the network. (iii) Geographical proximity, differences in economic development levels, industrial structure levels, and differences in urbanization have a significant impact on the formation of spatial association networks.

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 Research on the spatial association network structure for innovation efficiency of China’s new energy vehicle industry and its influencing factors

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255516
Author(s):  
Yining Zhang ◽  
Zhong Wu
Keyword(s):  
Spatial Correlation ◽  
Influencing Factors ◽  
Spatial Association ◽  
Correlation Network ◽  
Spatial Distance ◽  
Spatial Correlations ◽  
Analysis Model ◽  
New Energy ◽  
The Difference ◽  
New Energy Vehicle

It is of great significance to study the spatial network of the new energy vehicle (NEV) industry innovation efficiency and its factors to promote the rational allocation of innovative resources and the coordinated development of Chinese NEV industry. First, the Super Efficiency Data Envelope Analysis model is used to measure innovation efficiency in the NEV industry in Chinese provinces, and based on the results, the improved gravity model is applied to construct a spatial correlation network. Then, by applying social network analysis (SNA) to study NEV industry development node spatial correlations, we conclude that there is no overall hierarchical structure. The SNA are applied to examine spatial correlations with respect to NEV industry innovation efficiency in each province, and to analyze the role and position of each province in the spatial correlation network. Finally, the influencing factors of spatial correlation of the innovation efficiency of China’s NEV industry has been discussed. The result shows that the difference in spatial distance and R&D investment has a significant impact on the spatial correlation of the NEV industry.

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 and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3054 ◽  
Author(s):  
Zhen Li ◽  
Yanbin Li ◽  
Shuangshuang Shao
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Total Population ◽  
Industrial Structure ◽  
Global Climate ◽  
Primary Energy ◽  
Urbanization Level ◽  
Per Capita ◽  
Technology Level

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

scholarly journals Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 504
Author(s):  
Jinping Zhang ◽  
Qiuru Lu ◽  
Li Guan ◽  
Xiaoying Wang
Keyword(s):  
Energy Efficiency ◽  
Economic Development ◽  
Panel Data ◽  
Industrial Structure ◽  
Mainland China ◽  
Factors Influencing ◽  
Resource Endowment ◽  
Level Of Economic Development ◽  
Quantile Autoregression ◽  
Statistical Yearbook

This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating Moran’sI and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based on the provincial panel data (1998–2016) of mainland China (data source: China Statistical Yearbook, Statistical Yearbook of provinces), the findings indicate that level of economic development and industrial structure have a significant role in promoting energy efficient. Resource endowment, government intervention and energy efficiency show a negative correlation. However, the negative effect of government intervention is weakened with the increase of energy efficiency. Lastly, we compare the results of SQAR with that of ordinary spatial autoregression (SAR). The empirical result shows that the SQAR model is superior to SAR model in influencing factors analysis of energy efficiency.

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