scholarly journals The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2706 ◽  
Author(s):  
Feng Wang ◽  
Mengnan Gao ◽  
Juan Liu ◽  
Wenna Fan
Keyword(s):  
Network Structure ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Geographical Location ◽  
Network Effect ◽  
Quadratic Assignment ◽  
Spatial Correlations ◽  
Emission Data ◽  
Correlation Relationship ◽  
Four Blocks

Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

scholarly journals Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration

2019 ◽  
Vol 11 (5) ◽  
pp. 1444 ◽  
Author(s):  
Xintao Li ◽  
Dong Feng ◽  
Jian Li ◽  
Zaisheng Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Synergetic Effect ◽  
Urban Agglomeration ◽  
Network Density ◽  
Spatial Network ◽  
Network Efficiency ◽  
Emission Data ◽  
Network Characteristics ◽  
Network Hierarchy

Based on the carbon emission data in the Beijing–Tianjin–Hebei urban agglomeration from 2007 to 2016, this paper used the method of social network analysis (SNA) to investigate the spatial correlation network structure of the carbon emission. Then, by constructing the synergetic abatement effect model, we calculated the synergetic abatement effect in the cities and we empirically examined the influence of the spatial network characteristics on the synergetic abatement effect. The results show that the network density first increased from 0.205 in 2007 to 0.263 in 2014 and then decreased to 0.205 in 2016; the network hierarchy fluctuated around 0.710, and the minimum value of the network efficiency was 0.561, which indicates that the network hierarchy structure is stern and the network has good stability. Beijing and Tianjin are in the center of the carbon emission spatial network and play important “intermediary” and “bridge” roles that can have better control over other carbon emission spatial spillover relations between the cities, thus the spatial network of carbon emissions presents a typical “center–periphery” structure. The synergetic abatement effect increased from −2.449 in 2007 to 0.800 in 2011 and then decreased to −1.653 in 2016; the average synergetic effect was −0.550. This means that the overall synergetic level has a lot of room to grow. The carbon emission spatial network has a significant influence on the synergetic abatement effect, while increasing the network density and the network hierarchy. Decreasing the network efficiency will significantly enhance the synergetic abatement effect.

scholarly journals Association of Carbon Emissions and Circular Curve in Northwestern China

2019 ◽  
Vol 11 (4) ◽  
pp. 1156 ◽  
Author(s):  
Yaping Dong ◽  
Jinliang Xu ◽  
Menghui Li ◽  
Xingli Jia ◽  
Chao Sun
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Research Work ◽  
Field Tests ◽  
Quantitative Model ◽  
Northwestern China ◽  
Low Carbon ◽  
Emission Data ◽  
Circular Curve

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.

Carbon Emission Flow and Transfer through International Trade of Forest Products

2019 ◽  
Vol 65 (4) ◽  
pp. 439-451
Author(s):  
Prativa Shrestha ◽  
Changyou Sun
Keyword(s):  
International Trade ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Forest Products ◽  
Developed Countries ◽  
Total Emission ◽  
Forest Products Industry ◽  
Emission Data ◽  
Environmental Relations

Abstract The environmental impact of commodity trade has become a considerable concern in recent decades. In this study, carbon emissions embodied in forest products trade are examined through a multiregional input–output model. Compared with other industries, the forest products industry is clean with a small total emission and mean emission intensity. The paper sector is more substantial in total emission and dirtier in emission intensity than the wood sector. Most countries with extensive forest products trade have experienced declining consumption-based carbon emissions over 1995–2009, and all countries have become cleaner based on the emission intensity value. Carbon emissions embodied in international trade of forest products are about 25 percent of total emissions from production activities. Developing countries generally have much higher emission intensities than developed countries. Uncertainties in the carbon emission data have a larger impact than those in the intermediate and final consumption data. These findings are helpful for policymakers to understand the economic–environmental relations of forest products trade and to improve policy and agreement designs.

scholarly journals Road Traffic Status and Carbon Emission Estimation Methods

2020 ◽  
Vol 7 (2) ◽  
pp. p34
Author(s):  
Yang Kaixi
Keyword(s):  
Carbon Emissions ◽  
Traffic Congestion ◽  
Carbon Emission ◽  
Evaluation Method ◽  
Road Traffic ◽  
Estimation Methods ◽  
Vehicle Exhaust ◽  
Emission Data ◽  
Traffic Conditions ◽  
The Impact

This paper mainly studies the relationship between traffic status and carbon emission, and the evaluation method of carbon emission based on traffic status. First of all, the traffic status is defined. In this paper, the traffic status is divided into traffic congestion and unobstructed traffic. Then, this paper analyzes the influence of different traffic conditions on carbon emissions in the same fleet at the same time through the study of vehicle exhaust emissions in both the unobstructed and congested traffic conditions. The unobstructed section traffic is used to simulate the unobstructed traffic state, and the intersection is used to simulate the traffic congestion. Finally, the two kinds of carbon emission data are compared to obtain the impact of traffic status on carbon emissions.

scholarly journals The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective

2019 ◽  
Vol 16 (12) ◽  
pp. 2154
Author(s):  
Fei Ma ◽  
Yixuan Wang ◽  
Kum Fai Yuen ◽  
Wenlin Wang ◽  
Xiaodan Li ◽  
...  
Keyword(s):  
Social Network ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Structural Characteristics ◽  
Spatial Association ◽  
Spillover Effects ◽  
Three Dimensions ◽  
Quadratic Assignment ◽  
Central Regions ◽  
Association Effect

The association effect between provincial transportation carbon emissions has become an important issue in regional carbon emission management. This study explored the relationship and development trends associated with regional transportation carbon emissions. A social network method was used to analyze the structural characteristics of the spatial association of transportation carbon emissions. Indicators for each of the structural characteristics were selected from three dimensions: The integral network, node network, and spatial clustering. Then, this study established an association network for transportation carbon emissions (ANTCE) using a gravity model with China’s provincial data during the period of 2007 to 2016. Further, a block model (a method of partitioning provinces based on the information of transportation carbon emission) was used to group the ANTCE network of inter-provincial transportation carbon emissions to examine the overall association structure. There were three key findings. First, the tightness of China’s ANTCE network is growing, and its complexity and robustness are gradually increasing. Second, China’s ANTCE network shows a structural characteristic of “dense east and thin west.” That is, the transportation carbon emissions of eastern provinces in China are highly correlated, while those of central and western provinces are less correlated. Third, the eastern provinces belong to the two-way spillover or net benefit block, the central regions belong to the broker block, and the western provinces belong to the net spillover block. This indicates that the transportation carbon emissions in the western regions are flowing to the eastern and central regions. Finally, a regression analysis using a quadratic assignment procedure (QAP) was used to explore the spatial association between provinces. We found that per capita gross domestic product (GDP) and fixed transportation investments significantly influence the association and spillover effects of the ANTCE network. The research findings provide a theoretical foundation for the development of policies that may better coordinate carbon emission mitigation in regional transportation.

scholarly journals RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK

2018 ◽  
Vol XLII-3 ◽  
pp. 2291-2296
Author(s):  
T. Zhang ◽  
B. Zhou ◽  
S. Zhou ◽  
W. Yan
Keyword(s):  
Neural Network ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Global Climate ◽  
Prediction Method ◽  
Upward Trend ◽  
Emission Data ◽  
Elman Neural Network ◽  
Free Development ◽  
Per Capita

<p><strong>Abstract.</strong> Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’s particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents’ direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents’ direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory.</p>

scholarly journals Spatial Correlation Network and Driving Effect of Carbon Emission Intensity in China’s Construction Industry

2022 ◽  
Author(s):  
Zhenshuang Wang ◽  
Yanxin Zhou ◽  
Ning Zhao ◽  
Tao Wang ◽  
Zhong Sheng Zhang
Keyword(s):  
Construction Industry ◽  
Network Structure ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Network Efficiency ◽  
Emission Data ◽  
Structure Characteristics ◽  
Carbon Emission Intensity ◽  
Leading Role ◽  
Intermediary Role

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious &ldquo;core-edge&rdquo; state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of &ldquo;brokers&rdquo;. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

scholarly journals Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0236685
Author(s):  
Hui Jin
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Southern China ◽  
Global Climate ◽  
Population Data ◽  
Neural Network Models ◽  
Emission Data ◽  
Elman Neural Network

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

scholarly journals Characteristics, Decoupling Effect And Driving Factors of Regional Tourism's Carbon Emissions In China

2021 ◽  
Author(s):  
Guobao Xiong ◽  
Junhong Deng ◽  
Baogen Ding
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Driving Factors ◽  
Logarithmic Mean ◽  
Emission Data ◽  
Divisia Index ◽  
Its Regions ◽  
Decoupling Effect

Abstract Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

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