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 The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

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 Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis

2021 ◽  
Vol 18 (15) ◽  
pp. 8219
Author(s):  
Jianli Sui ◽  
Wenqiang Lv
Keyword(s):  
Economic Growth ◽  
Agricultural Economic ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Crop Production ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Lmdi Method ◽  
Carbon Emission Intensity ◽  
Decoupling Analysis

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

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.

scholarly journals Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

2020 ◽  
Vol 17 (20) ◽  
pp. 7541
Author(s):  
Jiaxing Pang ◽  
Hengji Li ◽  
Chengpeng Lu ◽  
Chenyu Lu ◽  
Xingpeng Chen
Keyword(s):  
Carbon Emissions ◽  
Agricultural Production ◽  
Emission Intensity ◽  
Regional Differences ◽  
Carbon Emission ◽  
Dynamic Evolution ◽  
Western Region ◽  
Carbon Emission Intensity ◽  
Emissions Intensity ◽  
The Difference

The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

Energy technological innovation and carbon emissions mitigation: evidence from China

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuping Cheng ◽  
Lingjie Meng ◽  
Lu Xing
Keyword(s):  
Technological Innovation ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Fossil Energy ◽  
Content Type ◽  
Carbon Emission Intensity ◽  
Emissions Mitigation ◽  
The Impact ◽  
Positive Effect

PurposeThe purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.Design/methodology/approachConditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.FindingsThe results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.Originality/valueConsidering that energy consumption is the main source of CO2 emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

Can the Financial Industry ‘Anchor’ Carbon Emission Reductions?

2022 ◽  
pp. 0958305X2110618
Author(s):  
Shuhong Wang ◽  
Xiaojing Yi
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Central Government ◽  
Economic Loss ◽  
Positive Influence ◽  
Financial Industry ◽  
Industry Development ◽  
Carbon Emission Intensity ◽  
Using Data

Existing research is ambiguous about the relationship between the financial industry development scale and carbon emission reduction targets. Therefore, using data from 30 provinces and municipalities directly under the central government (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2009–2018, this study divides the reduction targets into emission quantity and intensity to investigate this relationship. Using the improved STIRPAT equation, the pooled OLS and other estimation technique in robustness test, we found that the financial industry development scale is positively related to emission quantity and negatively related to emission intensity. The financial industry development scale inhibits carbon emission intensity through the mediating role of the technology market development degree, which also has a moderating effect on the scale. The study also discusses the regional differences in the scale's impact on carbon emission intensity, its compensation effect on the economic loss caused by carbon emissions, and the positive influence of policy implementation on carbon emission intensity. We provide suggestions to reduce carbon emissions and achieve carbon neutrality.

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