Research on Carbon Emission Characteristics and Influencing Factors of Logistics Industry: Based on EKC Model and LMDI Decomposition Method

2020 ◽  
pp. 405-417
Author(s):  
Chunyan Zhu ◽  
Xu Guo
Keyword(s):  
Influencing Factors ◽  
Decomposition Method ◽  
Carbon Emission ◽  
Emission Characteristics ◽  
Logistics Industry

scholarly journals Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5742
Author(s):  
Changyou Zhang ◽  
Wenyu Zhang ◽  
Weina Luo ◽  
Xue Gao ◽  
Bingchen Zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Added Value ◽  
Total Carbon ◽  
Carbon Intensity ◽  
Contribution Ratio ◽  
Logistics Industry ◽  
Per Capita ◽  
First Time

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China’s logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry.

Platform ecological circle for cold chain logistics enterprises: the value co-creation analysis

2020 ◽  
Vol 120 (4) ◽  
pp. 675-691 ◽  
Author(s):  
Benhong Peng ◽  
Yuanyuan Wang ◽  
Sardar Zahid ◽  
Guo Wei ◽  
Ehsan Elahi
Keyword(s):  
Influencing Factors ◽  
Resource Sharing ◽  
Structural Equation ◽  
Dynamic Capabilities ◽  
Positive Influence ◽  
Cold Chain ◽  
Enterprise Performance ◽  
Logistics Industry ◽  
Content Type ◽  
Logistics Enterprises

Purpose The purpose of this paper is to propose a framework of value co-creation in platform ecological circle for cold chain logistics enterprises to guide the transformation and development of cold chain logistics industry. Design/methodology/approach This paper establishes a conceptual framework for the research on the platform ecological circle in cold chain logistics, utilizes a structural equation model to investigate the influencing factors of the value co-creation of the platform ecological circle in the cold chain logistics enterprises and elaborates the internal relations between different influencing factors regarding the value co-creation and enterprises’ performance. Findings Results show that resource sharing in logistics platform ecological circle can stimulate the interaction among enterprises and this produces a positive influence on their dynamic capabilities, which, in turn, affects the they to work together to plan, implement and solve problems, so as to achieve the goal of improving enterprise performance. Practical implications The shared resources and value co-creation activities in the platform ecological circle are very important for the transformation and development of cold chain logistics enterprises. Therefore, enterprises should promote value co-creation through realizing resource sharing and creating a win-win cooperation mechanism. Originality/value This paper targets at incorporating the resource sharing in platform ecological circle for cold chain logistics enterprises, explores from an empirical perspective the role of the resource sharing in cold chain logistics enterprises in enhancing the dynamic capabilities of enterprises, thereby encouraging the value co-creation behavior, and ultimately boosts enterprise performance and stimulates business development.

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 Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

scholarly journals Inter-Regional Spillover of Carbon Emissions and Employment in China: Is It Positive or Negative?

2019 ◽  
Vol 11 (13) ◽  
pp. 3622 ◽  
Author(s):  
Wenbin Shao ◽  
Fangyi Li ◽  
Zhaoyang Ye ◽  
Zhipeng Tang ◽  
Wu Xie ◽  
...  
Keyword(s):  
Economic Development ◽  
Carbon Emissions ◽  
Decomposition Method ◽  
Carbon Emission ◽  
Spillover Effects ◽  
Efficiency Improvement ◽  
Input Output ◽  
Coastal Regions ◽  
Regional Trade ◽  
Positive Spillover

International and inter-regional trade in China has been promoted, the economic and environmental impacts of which are significant in regional development. In this paper, we analyzed the evolution of inter-regional spillover of carbon emissions and employment in China from 2007 to 2012 with structural decomposition method and multi-regional input-output tables. The index of carbon emission per employee (ICE) is designed and compared to indicate positive or negative spillover effects. We find that carbon emissions grow much more rapidly in interior regions than in coastal regions, due to spillover effects and own influences. Spillover effects rarely reduce the ICE of destination regions, but the own influences can decrease it in most regions. Although spillover may contribute to economic development in most regions, it is hardly a driver of efficiency improvement in destination regions. Based on these empirical findings, we put forward specific suggestions to improve the positive spillover effects on different kinds of regions.

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