Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing, China

2016 ◽  
Vol 61 ◽  
pp. 634-645 ◽  
Author(s):  
Zhaohua Wang ◽  
Yuantao Yang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Residential Consumption ◽  
Beijing China

scholarly journals Analysis of China’s Manufacturing Industry Carbon Lock-In and Its Influencing Factors

2020 ◽  
Vol 12 (4) ◽  
pp. 1502 ◽  
Author(s):  
Xia Wang ◽  
Lijun Zhang ◽  
Yaochen Qin ◽  
Jingfei Zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Ferrous Metal ◽  
Bohai Bay ◽  
High Carbon ◽  
Intergovernmental Panel ◽  
Resource Endowment ◽  
Regional Factors ◽  
Lock In

There are industry lock-in and regional lock-in phenomena in China’s manufacturing industry carbon emissions. However, the existing researches often focus on global carbon emissions, which is not adverse to finding the main problems of manufacturing industry carbon emissions. The biggest contributions of this study are the identification of the industry lock-in and regional lock-in of China’s manufacturing industry and the finding of the regional factors that affect the carbon lock-in of the manufacturing industry, which points out the direction for the low-carbon transformation of the local manufacturing industry. This paper is based on the IPCC (Intergovernmental Panel on Climate Change) carbon emissions coefficient method and energy consumption data from 2000 to 2016 to count the manufacturing industry carbon emissions of 30 provinces in China (except Hong Kong, Macao, Taiwan and Tibet). On this basis, the paper uses a spatial–temporal geographical weighted regression (GTWR) model to analysis the regional influencing factors of the high-carbon manufacturing industry. Results demonstrate that China’s high-carbon manufacturing industry mainly concentrates on the ferrous metal processing industry, non-metallic mineral manufacturing industry and other sectors. In addition, the carbon emissions of high-carbon manufacturing industries are mainly concentrated in Bohai Bay and the North China Plain. The industrial structure and economic scale are the main reasons for the regional carbon lock-in of the high-carbon manufacturing industry, and the strength of the lock-in has continued to increase. Resource endowment is a stable factor of carbon lock-in in high-carbon regions. Technological progress helps to unlock carbon, while foreign direct investment results in the enhancement of carbon regional lock-in. This study focuses on the regional factors of carbon lock-in in the manufacturing industry, hoping to provide decision support for the green development of China’s manufacturing industry.

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.

Large urban parks summertime cool and wet island intensity and its influencing factors in Beijing, China

2021 ◽  
pp. 127375
Author(s):  
Yilun Li ◽  
Shuxin Fan ◽  
Kun Li ◽  
Yue Zhang ◽  
Lingxu Kong ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Urban Parks ◽  
Beijing China

Analysis of the influencing factors of energy-related carbon emissions in Kazakhstan at different stages

2020 ◽  
Vol 27 (29) ◽  
pp. 36630-36638
Author(s):  
Chuanhe Xiong ◽  
Shuang Chen ◽  
Qun Gao ◽  
Liting Xu
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions

Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries

2014 ◽  
Vol 75 (S2) ◽  
pp. 319-332 ◽  
Author(s):  
Tijun Fan ◽  
Ruiling Luo ◽  
Haiyang Xia ◽  
Xiaopeng Li
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Lmdi Method ◽  
Petrochemical Industries

scholarly journals Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China

Land ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 158 ◽  
Author(s):  
Qianru Chen ◽  
Hualin Xie
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Spatial Differentiation ◽  
Utilization Efficiency ◽  
Added Value ◽  
Scale Model ◽  
Eastern Region ◽  
Cultivated Land ◽  
Land Utilization ◽  
Extension System

Cultivated land is closely related to national food security, rural economic development and social stability. The cultivated land pollution and carbon emissions caused by chemical fertilizers, pesticides, film residues, etc., in the process of cultivated land utilization pose a serious threat to the cultivated land ecosystem in China. The comprehensive analysis on the cultivated land green utilization efficiency (GUECL), its influencing factors, and optimization direction provides a valuable basis for the green utilization of cultivated land. Based on a panel data of 30 provinces (cities or districts) in China from 2001 to 2016, the GUECL in China under the constraints of pollution and carbon emissions was measured by using a super-efficient SBM-VRS (slack based model-variable return to scale) model. The influencing factors and optimization directions of the GUECL were analyzed through the Tobit model and slack values, respectively. The results show that the GUECL in China rose with fluctuations from 2001 to 2016. Since 2014, the eastern region has surpassed the western region and has become the region with the highest mean GUECL value. The room for resource conservation and pollution reduction varies in different regions of China. Farmers’ dependence on cultivated land and agricultural added value are positively related to the GUECL in China. Farmers’ occupational differentiation, agricultural machinery density, and agricultural disaster rate have had negative effects on the GUECL in China. The loss of the GUECL in China is mainly due to the redundancies of land input, pollution emission, and mechanical input. By analyzing these influencing factors and optimization directions, it is concluded that improving rural land transfer market and agricultural infrastructure construction, establishing a new agricultural technology extension system, and vigorously cultivating new professional farmers are the targeted measures to improve the GUECL.

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