Accounting and Characteristics Analysis of CO2 Emissions in Chinese Cities

2020 ◽  
Vol 08 (01) ◽  
pp. 2050004
Author(s):  
Xingmin WANG ◽  
Jing WU ◽  
Zheng WANG ◽  
Xiaoting JIA ◽  
Bing BAI
Keyword(s):  
Emission Intensity ◽  
Carbon Emission ◽  
North China ◽  
Spatial Separation ◽  
Yangtze River Delta ◽  
Policy Implications ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Carbon Policy ◽  
Per Capita

Accurate estimation of CO2 emissions is a prerequisite for scientific low-carbon emission policymaking. Based on 20 types of energy consumption data at the prefecture level in China, this paper re-estimates the CO2 emissions of 198 prefecture-level cities in 2016 by using the method of carbon emission coefficient. The spatial pattern and scale characteristics are analyzed, and the conclusions are as follows: (1) Overall, China’s urban CO2 emissions show a certain degree of spatial separation in terms of the total amount, per capita emissions, and emission intensity. Cities with the highest CO2 emissions in China are mainly concentrated in North China, East China and Chongqing, while cities with the highest per capita CO2 emissions and emission intensity are mainly concentrated in Northwest and North China. (2) Different types of cities have different CO2 emission characteristics. Resource-based cities have a higher total amount and emission intensity; tourism and underdeveloped cities both have lower values; while super-large-sized cities and many very-large-sized cities have higher CO2 emissions, but their emission intensities are usually lower; and no obvious rules are found in other cities. (3) Spatial analysis shows that cities with higher CO2 emissions are clustered. The Beijing–Tianjin–Hebei region, the Yangtze River Delta region, Shandong Province, and Shanxi–Henan–Anhui resource-producing areas are the agglomeration areas of high-emission cities. (4) Scale analysis shows that the characteristics of CO2 emissions at different scales are different. Provincial-level research can help to identify the environmental impact and total effect of carbon emissions, while urban-scale research is helpful to explore the diversity and phases of cities. Finally, based on the main conclusions of this study, the corresponding urban low-carbon policy implications are drawn.

scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

scholarly journals Symmetric and Asymmetric Impact of Poverty, Income Inequality, and Population on Carbon Emission in Pakistan: New Evidence From ARDL and NARDL Co-Integration

2021 ◽  
Vol 9 ◽  
Author(s):  
Salim Khan ◽  
Wang Yahong
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Environmental Degradation ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Policy Implications ◽  
Gdp Per Capita ◽  
Long Run ◽  
Asymmetric Impact ◽  
Per Capita

Several researchers have studied the relationship between poverty and environmental degradation, as these concerns are remained at top priority in achieving Sustainable Development Goals (SDGs). However, the symmetric and asymmetric impact of poverty and income inequality along with population and economic growth on carbon emissions (CO2e) has not been studied in the case of Pakistan. For this purpose, the short and long-run impact of poverty, income inequality, population, and GDP per capita on CO2e investigated by applying the Autoregressive Distributive Lag (ARDL) along with Non-linear Autoregressive Distributive Lag (NARDL) co-integration approach in the context of Pakistan for period 1971–2015. The symmetric results of the current study show poverty and population density along with GDP per capita increase carbon emissions in both the short and long-run, while income inequality has no impact on carbon emissions in the short-run. While in the long-run the symmetric results show that income inequality weakens environmental degradation in terms of carbon emissions. The analysis of NARDL also supports the results obtained from ARDL and suggests a positive effect of poverty, population, and economic growth on carbon emission in Pakistan. The empirical findings of the current study provide policy implications in light of the United Nation's SDGs for the development of Pakistan.

scholarly journals A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action

2019 ◽  
Vol 16 (22) ◽  
pp. 4452 ◽  
Author(s):  
Xuhui Ding ◽  
Zhongyao Cai ◽  
Qianqian Xiao ◽  
Suhui Gao
Keyword(s):  
Technological Innovation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Spillover Effects ◽  
The Yangtze River ◽  
Low Carbon ◽  
Spatial Spillover ◽  
Carbon Emission Intensity

It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.

scholarly journals Study on Dynamic Total Factor Carbon Emission Efficiency in China’s Urban Agglomerations

2020 ◽  
Vol 12 (7) ◽  
pp. 2675
Author(s):  
Fan Zhang ◽  
Gui Jin ◽  
Junlong Li ◽  
Chao Wang ◽  
Ning Xu
Keyword(s):  
Pearl River Delta ◽  
Yangtze River ◽  
Carbon Emission ◽  
Yangtze River Delta ◽  
River Delta ◽  
Pearl River ◽  
Low Carbon ◽  
Frontier Analysis ◽  
Urban Agglomerations ◽  
Emission Efficiency

The scale effect of urbanization on improving carbon emission efficiency and achieving low-carbon targets is an important topic in urban research. Using dynamic panel data from 64 prefecture-level cities in four typical urban agglomerations in China from 2006 to 2016, this paper constructed a stochastic frontier analysis model to empirically measure the city-level total-factor carbon emission efficiency index (TCEI) at different stages of urbanization and to identify rules governing its spatiotemporal evolution. We quantitatively analyzed the influences and functional characteristics of TCEI in the four urban agglomerations of Pearl River Delta, Beijing-Tianjin-Hebei, the Yangtze River Delta, and Chengdu-Chongqing. Results show that the TCEI at different stages of urbanization in these urban agglomerations is increasing year by year. The overall city-level TCEI was ranked as follows: Pearl River Delta > Beijing-Tianjin-Hebei > Yangtze River Delta > Chengdu-Chongqing. Improvements in the level of economic development and urbanization will help achieve low-carbon development in a given urban agglomeration. The optimization of industrial structure and improvement of ecological environment will help curb carbon emissions. This paper provides decision-making references for regional carbon emission reduction from optimizing industrial and energy consumption structures and improving energy efficiency.

scholarly journals Energy saving and emission reduction of fossil energy based on low carbon economy and its consumption structure optimization

2019 ◽  
Vol 14 (3) ◽  
pp. 381-385 ◽  
Author(s):  
Yan Li ◽  
Guilin Dai
Keyword(s):  
Energy Saving ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Total Carbon ◽  
Energy Structure ◽  
Low Carbon ◽  
Parallel Acquisition ◽  
Low Carbon Economy ◽  
Carbon Emission Intensity

Abstract Energy saving and emission reduction have been not only a slogan but also a policy in this modern society where the phenomenon of greenhouse is exacerbated. In this study, calculation method of carbon emission and integrated parallel acquisition technique (IPAT) scenario prediction model were combined to predict the changes of total carbon emissions, energy structure distribution, and carbon emission intensity under three measures of energy saving and emission reduction in the next ten years in Shandong, China. The results showed that the total carbon emission increased year by year, and the coal ratio and carbon emission intensity decreased under the natural scenario; the total carbon emission in the weakly constrained scenario would increase annually until 2029, the amplitude was smaller than that of the natural scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than that of the natural scenario. Under the strongly constrained scenario, the total carbon emission would increase annually before 2025, and the amplitude was smaller than the weakly constrained scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than the weakly constrained scenario.

scholarly journals Spatiotemporal Analysis of Land Use Patterns on Carbon Emissions in China

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 141
Author(s):  
Qiaowen Lin ◽  
Lu Zhang ◽  
Bingkui Qiu ◽  
Yi Zhao ◽  
Chao Wei
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Local Governments ◽  
Carbon Emission ◽  
Land Development ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Carbon Emission Intensity ◽  
Per Capita

Nowadays, China is the world’s second largest economy and largest carbon emitter. This paper calculates the carbon emission intensity and the carbon emissions per capita of land use in 30 provinces at the national level in China from 2006 to 2016. A spatial correlation model is used to explore its spatiotemporal features. The results show that (1) China’s land use carbon emissions continued to grow from 2006 to 2016. The spatial heterogeneity of carbon emission intensity of land use initially decreased and then increased during this period. The carbon emission of land use pattern reached a peak in 2015 and the land use carbon emission intensity was relatively lower in east China; (2) southern China accounts for a majority of the total Chinese carbon sink. Better economic structure, land use structure and industrial structure will lead to lower carbon emission intensity of land use; (3) carbon emissions per capita of land use in China are affected not only by land development intensity, urbanization level, and energy consumption structure, but also by the population policy. It is significant to formulate differentiated energy and land use policies according to local conditions. This study not only provides a scientific basis for formulating different carbon emission mitigation policies for the local governments in China, but also provides theoretical reference for other developing countries for sustainable development. It contributes to the better understanding of the land use patterns on carbon emissions in China.

Mining and Analyzing Energy Layout on Carbon Emission Intensities of Industrial Sectors

2013 ◽  
Vol 807-809 ◽  
pp. 857-860
Author(s):  
Zuo Zhi Li
Keyword(s):  
Energy Consumption ◽  
Policy Making ◽  
Carbon Emission ◽  
Methodological Framework ◽  
Low Carbon ◽  
Carbon Policy ◽  
Consumption Structure ◽  
Industrial Sectors ◽  
Industrial Distribution ◽  
Data Group

Energy consumption structure optimum is gradually discussed in recent literatures. Based on hierarchical clustering of optimally close to content demand of data group mine and analysis, industrial sectors layout on carbon emission intensity is researched. Computed carbon emission drawn support from IPCC methodological framework, formed carbon emission intensities of emissions divided by sectors GDP respectively, and transformed calculated figures into CDF of the continuous uniform distribution to cultivate the standardized data. Resulting of the case presents that there are two categories with types of v and inversed v after mining and analyzing 37 industrial sectors data in 2006-2011. Findings are that 39% annual max paired difference of emission intensities is appeared, and the divergence of energy consumption structure is significantly obtained, which is conducive to the whole industrial distribution of low carbon policy-making.

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